├── .gitignore ├── .idea ├── .name ├── data-mining-course.iml ├── dataminingcource.iml ├── encodings.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── DataMingAssignments@NJU-CS.pdf ├── README.md ├── __init__.py ├── assignment1 ├── Assignment1.pdf ├── EnglishTextDataProcessing │ ├── __init__.py │ ├── dataset.py │ ├── main.py │ ├── preprocess.py │ └── tfidf.py ├── README.txt ├── __init__.py ├── report.doc ├── report.pdf └── result │ ├── 1. Active Learning │ ├── 10. Ranking │ ├── 11. Reinforcement Learning │ ├── 12. Supervised Learning │ ├── 13. Theory │ ├── 14. Unsupervised and Semi-Supervised Learning │ ├── 15. Others │ ├── 2. Applications │ ├── 3. Bayesian Learning and Graphical Model │ ├── 4. Deep Learning │ ├── 5. Ensemble and Crowdsourcing │ ├── 6. Feature Learning │ ├── 7. Kernel Methods │ ├── 8. Online Learning │ ├── 9. Optimization │ └── word_list.txt ├── assignment2 ├── Assignment2.pdf ├── DimReductionwithSklearn.py ├── README.txt ├── __init__.py ├── isomap.py ├── knn.py ├── main.py ├── pca.py ├── report.doc ├── report.pdf ├── result.txt └── svd.py ├── assignment3 ├── Assignment3.pdf ├── Document clustering based on non-negative matrix factorization.pdf ├── README.txt ├── __init__.py ├── belkin01laplacian.pdf ├── kmedoids.py ├── report.doc ├── report.pdf ├── result.txt ├── run.py ├── score.py └── spectralclustering.py ├── assignment4 ├── Assignment4.pdf ├── README.txt ├── __init__.py ├── figure │ ├── covtype_SGD-LogLossRegression-Error-Rate_错误率.png │ ├── covtype_SGD-LogLossRegression-Objective-Function_目标函数.png │ ├── covtype_SGD-RidgeRegression-Error-Rate_错误率.png │ ├── covtype_SGD-RidgeRegression-Objective-Function_目标函数.png │ ├── dataset1-a9a_SGD-LogLossRegression-Error-Rate_错误率.png │ ├── dataset1-a9a_SGD-LogLossRegression-Objective-Function_目标函数.png │ ├── dataset1-a9a_SGD-RidgeRegression-Error-Rate_错误率.png │ └── dataset1-a9a_SGD-RidgeRegression-Objective-Function_目标函数.png ├── logisticregression.py ├── main.py ├── plotfigure.py ├── report.doc ├── report.pdf ├── result.txt ├── ridgeregression.py └── sgdwithsklearn.py ├── assignment5 ├── Assignment5.pdf ├── README.txt ├── __init__.py ├── adaboost.py ├── kfolddataset.py ├── main.py ├── naivebayes.py ├── report.doc └── report.pdf ├── assignment6 ├── Assignment6.pdf ├── Data - Santander Product Recommendation _ Kaggle.pdf ├── README.txt ├── __init__.py ├── report.doc ├── report.pdf └── santander.py ├── data ├── BinaryDatasets │ ├── sonar-test.txt │ ├── sonar-train.txt │ ├── splice-test.txt │ └── splice-train.txt ├── Clustering │ ├── german.txt │ └── mnist.txt ├── EnsembleLearning │ ├── breast-cancer-assignment5.txt │ └── german-assignment5.txt ├── ICML │ ├── 1. Active Learning │ │ ├── Active Transfer Learning under Model Shift.txt │ │ ├── Diagnosis determination.txt │ │ ├── Gaussian Process Optimization with Mutual Information.txt │ │ ├── Hard-Margin Active Linear Regression.txt │ │ └── Nonmyopic $epsilon$-Bayes-Optimal Active Learning of Gaussian Processes.txt │ ├── 10. Ranking │ │ ├── A Consistent Histogram Estimator for Exchangeable Graph Models.txt │ │ ├── Coherent Matrix Completion.txt │ │ ├── Distributional Rank Aggregation and an Axiomatic Analysis.txt │ │ ├── Exponential Family Matrix Completion under Structural Constraints.txt │ │ ├── Generalization error bounds for learning to rank Does the length of document lists matter.txt │ │ ├── MRA-based Statistical Learning from Incomplete Rankings.txt │ │ ├── Near-Optimal Joint Object Matching via Convex Relaxation.txt │ │ ├── Preference Completion Large-scale Collaborative Ranking from Pairwise Comparisons.txt │ │ ├── Ranking from Stochastic Pairwise Preferences Recovering Condorcet Winners and Tournament Solution Sets at the Top.txt │ │ ├── Spectral MLE Top-K Rank Aggregation from Pairwise Comparisons.txt │ │ ├── Square Deal.txt │ │ ├── Universal Matrix Completion.txt │ │ └── desktop.ini │ ├── 11. Reinforcement Learning │ │ ├── A Deeper Look at Planning as Learning from Replay.txt │ │ ├── A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits.txt │ │ ├── A new Q(lambda) with interim forward view and Monte Carlo equivalence.txt │ │ ├── Abstraction Selection in Model-based Reinforcement Learning.txt │ │ ├── Active Learning of Parameterized Skills.txt │ │ ├── Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games.txt │ │ ├── Approximate Policy Iteration Schemes.txt │ │ ├── Bias in Natural Actor-Critic Algorithms.txt │ │ ├── Deterministic Policy Gradient Algorithms.txt │ │ ├── Fictitious Self-Play in Extensive-Form Games.txt │ │ ├── GeNGA.txt │ │ ├── High Confidence Policy Improvement.txt │ │ ├── Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning.txt │ │ ├── Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing.txt │ │ ├── Model-Based Relational RL When Object Existence is Partially Observable.txt │ │ ├── Non-Stationary Approximate Modified Policy Iteration.txt │ │ ├── Off-policy Model-based Learning under Unknown Factored Dynamics.txt │ │ ├── On TD 0 with function approximation Concentration bounds and a centered variant with exponential convergence.txt │ │ ├── On the Rate of Convergence and Error Bounds for LSTD lambda.txt │ │ ├── PAC-inspired Option Discovery in Lifelong Reinforcement Learning.txt │ │ ├── Programming by Feedback.txt │ │ ├── Robust partially observable Markov decision process.txt │ │ ├── Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret.txt │ │ ├── Scaling Up Robust MDPs using Function Approximation.txt │ │ ├── Time-Regularized Interrupting Options (TRIO).txt │ │ ├── Training Deep Convolutional Neural Networks to Play Go.txt │ │ ├── True Online TD(lambda).txt │ │ ├── Trust Region Policy Optimization.txt │ │ ├── Universal Value Function Approximators.txt │ │ └── desktop.ini │ ├── 12. Supervised Learning │ │ ├── A New Generalized Error Path Algorithm for Model Selection.txt │ │ ├── A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data.txt │ │ ├── Attribute Efficient Linear Regression with Distribution-Dependent Sampling.txt │ │ ├── Condensed Filter Tree for Cost-Sensitive Multi-Label Classification.txt │ │ ├── Consistent Multiclass Algorithms for Complex Performance Measures.txt │ │ ├── Dynamic Sensing Better Classification under Acquisition Constraints.txt │ │ ├── Entropy evaluation based on confidence intervals of frequency estimates Application to the learning of decision trees.txt │ │ ├── Feature-Budgeted Random Forest.txt │ │ ├── GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare.txt │ │ ├── Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting.txt │ │ ├── Learning from Corrupted Binary Labels via Class-Probability Estimation.txt │ │ ├── Multi-instance multi-label learning in the presence of novel class instances.txt │ │ ├── On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property.txt │ │ ├── Robust Learning under Uncertain Test Distributions.txt │ │ ├── Support Matrix Machines.txt │ │ ├── The Coherent Loss Function for Classification.txt │ │ └── desktop.ini │ ├── 13. Theory │ │ ├── (Near) Dimension Independent Risk Bounds for Differentially Private Learning.txt │ │ ├── A Theoretical Analysis of Metric Hypothesis Transfer Learning.txt │ │ ├── Anti-differentiating approximation algorithms.txt │ │ ├── Classification with Low Rank and Missing Data.txt │ │ ├── Concentration in unbounded metric spaces and algorithmic stability.txt │ │ ├── Convergence rate of Bayesian tensor estimator and its minimax optimality.txt │ │ ├── Convex Calibrated Surrogates for Hierarchical Classification.txt │ │ ├── Deterministic Independent Component Analysis.txt │ │ ├── Distributed Estimation of Generalized Matrix Rank Efficient Algorithms and Lower Bounds.txt │ │ ├── Entropy-Based Concentration Inequalities for Dependent Variables.txt │ │ ├── Finding Dense Subgraphs via Low-Rank Bilinear Optimization.txt │ │ ├── Heavy-tailed regression with a generalized median-of-means.txt │ │ ├── K-means recovers ICA filters when independent components are sparse.txt │ │ ├── Learnability of the Superset Label Learning Problem.txt │ │ ├── Learning Polynomials with Neural Networks.txt │ │ ├── Learning Theory and Algorithms for revenue optimization in second price auctions with reserve.txt │ │ ├── Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians.txt │ │ ├── Maximum Margin Multiclass Nearest Neighbors.txt │ │ ├── Multi-period Trading Prediction Markets with Connections to Machine Learning.txt │ │ ├── Nonnegative Sparse PCA with Provable Guarantees.txt │ │ ├── Optimizing Non-decomposable Performance Measures A Tale of Two Classes.txt │ │ ├── Provable Bounds for Learning Some Deep Representations.txt │ │ ├── Risk and Regret of Hierarchical Bayesian Learners.txt │ │ ├── Sample Efficient Reinforcement Learning with Gaussian Processes.txt │ │ ├── Scaling Up Approximate Value Iteration with Options.txt │ │ ├── Surrogate Functions for Maximizing Precision at the Top.txt │ │ ├── The Ladder A Reliable Leaderboard for Machine Learning Competitions.txt │ │ ├── Theory of Dual-sparse Regularized Randomized Reduction.txt │ │ ├── Towards Minimax Online Learning with Unknown Time Horizon.txt │ │ └── desktop.ini │ ├── 14. Unsupervised and Semi-Supervised Learning │ │ ├── A Convex Optimization Framework for Bi-Clustering.txt │ │ ├── A Probabilistic Model for Dirty Multi-task Feature Selection.txt │ │ ├── A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning.txt │ │ ├── A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data.txt │ │ ├── Active Detection via Adaptive Submodularity.txt │ │ ├── Active Nearest Neighbors in Changing Environments.txt │ │ ├── Alpha-Beta Divergences Discover Micro and Macro Structures in Data.txt │ │ ├── An Information Geometry of Statistical Manifold Learning.txt │ │ ├── An embarrassingly simple approach to zero-shot learning.txt │ │ ├── Anomaly Ranking as Supervised Bipartite Ranking.txt │ │ ├── Asymmetric Transfer Learning with Deep Gaussian Processes.txt │ │ ├── Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts.txt │ │ ├── Binary Embedding Fundamental Limits and Fast Algorithm.txt │ │ ├── Bipartite Edge Prediction via Transductive Learning over Product Graphs.txt │ │ ├── Clustering in the Presence of Background Noise.txt │ │ ├── Context-based Unsupervised Data Fusion for Decision Making.txt │ │ ├── Convergence rates for persistence diagram estimation in Topological Data Analysis.txt │ │ ├── Convex Formulation for Learning from Positive and Unlabeled Data.txt │ │ ├── Convex Learning of Multiple Tasks and their Structure.txt │ │ ├── Coordinate-descent for learning orthogonal matrices through Givens rotations.txt │ │ ├── Deep Unsupervised Learning using Nonequilibrium Thermodynamics.txt │ │ ├── Demystifying Information-Theoretic Clustering.txt │ │ ├── Entropic Graph-based Posterior Regularization.txt │ │ ├── Fast Computation of Wasserstein Barycenters.txt │ │ ├── Geodesic Distance Function Learning via Heat Flow on Vector Fields.txt │ │ ├── Graph-based Semi-supervised Learning.txt │ │ ├── Hashing for Distributed Data.txt │ │ ├── Hidden Markov Anomaly Detection.txt │ │ ├── Hierarchical Dirichlet Scaling Process.txt │ │ ├── Hierarchical Quasi-Clustering Methods for Asymmetric Networks.txt │ │ ├── Information Geometry and Minimum Description Length Networks.txt │ │ ├── Landmarking Manifolds with Gaussian Processes.txt │ │ ├── Learning Local Invariant Mahalanobis Distances.txt │ │ ├── Local Ordinal Embedding.txt │ │ ├── Local algorithms for interactive clustering.txt │ │ ├── Max-Margin Infinite Hidden Markov Models.txt │ │ ├── Multi-Task Learning for Subspace Segmentation.txt │ │ ├── Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization.txt │ │ ├── Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer.txt │ │ ├── On Symmetric and Asymmetric LSHs for Inner Product Search.txt │ │ ├── On learning to localize objects with minimal supervision.txt │ │ ├── Online Bayesian Passive-Aggressive Learning.txt │ │ ├── Optimization Equivalence of Divergences Improves Neighbor Embedding.txt │ │ ├── Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices.txt │ │ ├── Spectral Clustering via the Power Method Provably.txt │ │ ├── Standardized Mutual Information for Clustering Comparisons.txt │ │ ├── Structural Maxent Models.txt │ │ ├── Structured Generative Models of Natural Source Code.txt │ │ ├── Subsampling Methods for Persistent Homology.txt │ │ ├── The f-Adjusted Graph Laplacian.txt │ │ ├── Transductive Learning with Multi-class Volume Approximation.txt │ │ ├── Two-Stage Metric Learning.txt │ │ ├── Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations.txt │ │ ├── Von Mises-Fisher Clustering Models.txt │ │ ├── Wasserstein Propagation for Semi-Supervised Learning.txt │ │ ├── Yinyang K-Means A Drop-In Replacement of the Classic K-Means with Consistent Speedup.txt │ │ └── desktop.ini │ ├── 15. Others │ │ ├── A Bayesian Wilcoxon signed-rank test based on the Dirichlet process.txt │ │ ├── Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction.txt │ │ ├── Alternating Minimization for Mixed Linear Regression.txt │ │ ├── Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.txt │ │ ├── Computing Parametric Ranking Models via Rank-Breaking.txt │ │ ├── Concept Drift Detection Through Resampling.txt │ │ ├── Consistency of Causal Inference under the Additive Noise Model.txt │ │ ├── Counterfactual Risk Minimization Learning from Logged Bandit Feedback.txt │ │ ├── Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning.txt │ │ ├── Discovering Temporal Causal Relations from Subsampled Data.txt │ │ ├── Estimating Latent-Variable Graphical Models using Moments and Likelihoods.txt │ │ ├── Generalized Exponential Concentration Inequality for Renyi Divergence Estimation.txt │ │ ├── High Order Regularization for Semi-Supervised Learning of Structured Output Problems.txt │ │ ├── How Hard is Inference for Structured Prediction.txt │ │ ├── Learning Deep Structured Models.txt │ │ ├── Learning Fast-Mixing Models for Structured Prediction.txt │ │ ├── Learning Mixtures of Linear Classifiers.txt │ │ ├── Learning Scale-Free Networks by Dynamic Node Specific Degree Prior.txt │ │ ├── Learning Submodular Losses with the Lovasz Hinge.txt │ │ ├── Learning to Search Better than Your Teacher.txt │ │ ├── Marginal Structured SVM with Hidden Variables.txt │ │ ├── Methods of Moments for Learning Stochastic Languages.txt │ │ ├── On Robustness and Regularization of Structural Support Vector Machines.txt │ │ ├── Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs.txt │ │ ├── PeakSeg constrained optimal segmentation and supervised penalty learning for peak detection in count data.txt │ │ ├── Removing systematic errors for exoplanet search via latent causes.txt │ │ ├── Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications.txt │ │ ├── Spectral Regularization for Max-Margin Sequence Tagging.txt │ │ ├── Structured Prediction of Network Response.txt │ │ ├── Telling cause from effect in deterministic linear dynamical systems.txt │ │ ├── The Falling Factorial Basis and Its Statistical Applications.txt │ │ ├── Towards a Learning Theory of Cause-Effect Inference.txt │ │ └── desktop.ini │ ├── 2. Applications │ │ ├── A Clockwork RNN.txt │ │ ├── A Divide and Conquer Framework for Distributed Graph Clustering.txt │ │ ├── A Fast Variational Approach for Learning Markov Random Field Language Models.txt │ │ ├── A Linear Dynamical System Model for Text.txt │ │ ├── A Multitask Point Process Predictive Model.txt │ │ ├── Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams.txt │ │ ├── An Aligned Subtree Kernel for Weighted Graphs.txt │ │ ├── Approval Voting and Incentives in Crowdsourcing.txt │ │ ├── Asymptotically consistent estimation of the number of change points in highly dependent time series.txt │ │ ├── Bayesian Multiple Target Localization.txt │ │ ├── Bimodal Modelling of Source Code and Natural Language.txt │ │ ├── Boosting multi-step autoregressive forecasts.txt │ │ ├── Budget Allocation Problem with Multiple Advertisers A Game Theoretic View.txt │ │ ├── Community Detection Using Time-Dependent Personalized PageRank.txt │ │ ├── Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM.txt │ │ ├── Compositional Morphology for Word Representations and Language Modelling.txt │ │ ├── Consistent estimation of dynamic and multi-layer block models.txt │ │ ├── Correlation Clustering in Data Streams.txt │ │ ├── Differentially Private Bayesian Optimization.txt │ │ ├── Discovering Latent Network Structure in Point Process Data.txt │ │ ├── Distributed Representations of Sentences and Documents.txt │ │ ├── Effective Bayesian Modeling of Groups of Related Count Time Series.txt │ │ ├── Efficient Dimensionality Reduction for High-Dimensional Network Estimation.txt │ │ ├── Efficient Label Propagation.txt │ │ ├── Estimating Diffusion Network Structures.txt │ │ ├── From Word Embeddings To Document Distances.txt │ │ ├── Functional Subspace Clustering with Application to Time Series.txt │ │ ├── Global graph kernels using geometric embeddings.txt │ │ ├── HawkesTopic A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades.txt │ │ ├── Inferring Graphs from Cascades A Sparse Recovery Framework.txt │ │ ├── Influence Function Learning in Information Diffusion Networks.txt │ │ ├── Is Feature Selection Secure against Training Data Poisoning.txt │ │ ├── Joint Inference of Multiple Label Types in Large Networks.txt │ │ ├── Learning Character-level Representations for Part-of-Speech Tagging.txt │ │ ├── Learning Graphs with a Few Hubs.txt │ │ ├── Learning Modular Structures from Network Data and Node Variables.txt │ │ ├── Learning Parametric-Output HMMs with Two Aliased States.txt │ │ ├── Learning Word Representations with Hierarchical Sparse Coding.txt │ │ ├── Learning from Contagion (Without Timestamps).txt │ │ ├── Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks.txt │ │ ├── Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification.txt │ │ ├── Long Short-Term Memory Over Recursive Structures.txt │ │ ├── Modeling Correlated Arrival Events with Latent Semi-Markov Processes.txt │ │ ├── Moderated and Drifting Linear Dynamical Systems.txt │ │ ├── Multimodal Neural Language Models.txt │ │ ├── Phrase-based Image Captioning.txt │ │ ├── Privacy for Free Posterior Sampling and Stochastic Gradient Monte Carlo.txt │ │ ├── Rademacher Observations Private Data and Boosting.txt │ │ ├── Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes.txt │ │ ├── Scalable Model Selection for Large-Scale Factorial Relational Models.txt │ │ ├── Stochastic Variational Inference for Bayesian Time Series Models.txt │ │ ├── The Composition Theorem for Differential Privacy.txt │ │ ├── Threshold Influence Model for Allocating Advertising Budgets.txt │ │ ├── Towards End-To-End Speech Recognition with Recurrent Neural Networks.txt │ │ ├── Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models.txt │ │ ├── Weighted Graph Clustering with Non-Uniform Uncertainties.txt │ │ └── desktop.ini │ ├── 3. Bayesian Learning and Graphical Model │ │ ├── A Bayesian nonparametric procedure for comparing algorithms.txt │ │ ├── A Compilation Target for Probabilistic Programming Languages.txt │ │ ├── A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models.txt │ │ ├── A Discriminative Latent Variable Model for Online Clustering.txt │ │ ├── A Hybrid Approach for Probabilistic Inference using Random Projections.txt │ │ ├── A PAC-Bayesian bound for Lifelong Learning.txt │ │ ├── A reversible infinite HMM using normalised random measures.txt │ │ ├── A trust-region method for stochastic variational inference with applications to streaming data.txt │ │ ├── Admixture of Poisson MRFs.txt │ │ ├── Affinity Weighted Embedding.txt │ │ ├── Agnostic Bayesian Learning of Ensembles.txt │ │ ├── An Efficient Approach for Assessing Hyperparameter Importance.txt │ │ ├── An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process.txt │ │ ├── Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process.txt │ │ ├── Atomic Spatial Processes.txt │ │ ├── Austerity in MCMC Land.txt │ │ ├── Automated inference of point of view from user interactions in collective intelligence venues.txt │ │ ├── Bayesian Max-margin Multi-Task Learning with Data Augmentation.txt │ │ ├── Bayesian Optimization with Inequality Constraints.txt │ │ ├── Bayesian and Empirical Bayesian Forests.txt │ │ ├── Beta Diffusion Trees.txt │ │ ├── Celeste Variational inference for a generative model of astronomical images.txt │ │ ├── DP-space Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics.txt │ │ ├── Dealing with small data On the generalization of context trees.txt │ │ ├── Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes.txt │ │ ├── Discrete Chebyshev Classifiers.txt │ │ ├── Distributed Inference for Dirichlet Process Mixture Models.txt │ │ ├── Distributed Stochastic Gradient MCMC.txt │ │ ├── Doubly Stochastic Variational Bayes for non-Conjugate Inference.txt │ │ ├── Efficient Continuous-Time Markov Chain Estimation.txt │ │ ├── Efficient Training of LDA on a GPU by Mean-for-Mode Estimation.txt │ │ ├── Exchangeable Variable Models.txt │ │ ├── Exponential Integration for Hamiltonian Monte Carlo.txt │ │ ├── Fast Allocation of Gaussian Process Experts.txt │ │ ├── Filtering with Abstract Particles.txt │ │ ├── Fixed-point algorithms for learning determinantal point processes.txt │ │ ├── Gaussian Approximation of Collective Graphical Models.txt │ │ ├── Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations.txt │ │ ├── Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm.txt │ │ ├── Gradient-based Hyperparameter Optimization through Reversible Learning.txt │ │ ├── Hamiltonian Monte Carlo Without Detailed Balance.txt │ │ ├── Harmonic Exponential Families on Manifolds.txt │ │ ├── High Dimensional Bayesian Optimisation and Bandits via Additive Models.txt │ │ ├── Inference in a Partially Observed Queuing Model with Applications in Ecology.txt │ │ ├── Inferning with High Girth Graphical Models.txt │ │ ├── Input Warping for Bayesian Optimization of Non-Stationary Functions.txt │ │ ├── JUMP-Means Small-Variance Asymptotics for Markov Jump Processes.txt │ │ ├── Kernel Adaptive Metropolis-Hastings.txt │ │ ├── Large-scale Distributed Dependent Nonparametric Trees.txt │ │ ├── Latent Topic Networks A Versatile Probabilistic Programming Framework for Topic Models.txt │ │ ├── Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data.txt │ │ ├── Learning Latent Variable Gaussian Graphical Models.txt │ │ ├── Learning Sum-Product Networks with Direct and Indirect Variable Interactions.txt │ │ ├── Learning the Irreducible Representations of Commutative Lie Groups.txt │ │ ├── Learning the Parameters of Determinantal Point Process Kernels.txt │ │ ├── Linear and Parallel Learning of Markov Random Fields.txt │ │ ├── Low-density Parity Constraints for Hashing-Based Discrete Integration.txt │ │ ├── Manifold-valued Dirichlet Processes.txt │ │ ├── Markov Chain Monte Carlo and Variational Inference Bridging the Gap.txt │ │ ├── Markov Mixed Membership Models.txt │ │ ├── Memory (and Time) Efficient Sequential Monte Carlo.txt │ │ ├── Message Passing for Collective Graphical Models.txt │ │ ├── Metadata Dependent Mondrian Processes.txt │ │ ├── Min-Max Problems on Factor Graphs.txt │ │ ├── Multiple Testing under Dependence via Semiparametric Graphical Models.txt │ │ ├── Nested Sequential Monte Carlo Methods.txt │ │ ├── Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood.txt │ │ ├── Nonparametric Estimation of Renyi Divergence and Friends.txt │ │ ├── On Measure Concentration of Random Maximum A-Posteriori Perturbations.txt │ │ ├── On Modelling Non-linear Topical Dependencies.txt │ │ ├── On the Relationship between Sum-Product Networks and Bayesian Networks.txt │ │ ├── Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models.txt │ │ ├── Ordinal Mixed Membership Models.txt │ │ ├── Pitfalls in the use of Parallel Inference for the Dirichlet Process.txt │ │ ├── Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.txt │ │ ├── Preserving Modes and Messages via Diverse Particle Selection.txt │ │ ├── Probabilistic Partial Canonical Correlation Analysis.txt │ │ ├── Proteins Particles and Pseudo-Max-Marginals A Submodular Approach.txt │ │ ├── Putting MRFs on a Tensor Train.txt │ │ ├── Rebuilding Factorized Information Criterion Asymptotically Accurate Marginal Likelihood.txt │ │ ├── Rectangular Tiling Process.txt │ │ ├── Reified Context Models.txt │ │ ├── Robust RegBayes.txt │ │ ├── Safe Exploration for Optimization with Gaussian Processes.txt │ │ ├── Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors.txt │ │ ├── Scalable Bayesian Optimization Using Deep Neural Networks.txt │ │ ├── Scalable Deep Poisson Factor Analysis for Topic Modeling.txt │ │ ├── Scalable Semidefinite Relaxation for Maximum A Posterior Estimation.txt │ │ ├── Scalable Variational Inference in Log-supermodular Models.txt │ │ ├── Scalable and Robust Bayesian Inference via the Median Posterior.txt │ │ ├── Skip Context Tree Switching.txt │ │ ├── Spherical Hamiltonian Monte Carlo for Constrained Target Distributions.txt │ │ ├── Stochastic Gradient Hamiltonian Monte Carlo.txt │ │ ├── The Benefits of Learning with Strongly Convex Approximate Inference.txt │ │ ├── The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling.txt │ │ ├── The Inverse Regression Topic Model.txt │ │ ├── Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data.txt │ │ ├── Towards scaling up Markov chain Monte Carlo.txt │ │ ├── Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis.txt │ │ ├── Variational Inference for Sequential Distance Dependent Chinese Restaurant Process.txt │ │ ├── Variational Inference with Normalizing Flows.txt │ │ ├── Vector-Space Markov Random Fields via Exponential Families.txt │ │ └── desktop.ini │ ├── 4. Deep Learning │ │ ├── A Deep and Tractable Density Estimator.txt │ │ ├── An Empirical Exploration of Recurrent Network Architectures.txt │ │ ├── Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift.txt │ │ ├── BilBOWA Fast Bilingual Distributed Representations without Word Alignments.txt │ │ ├── Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions.txt │ │ ├── Compressing Neural Networks with the Hashing Trick.txt │ │ ├── DRAW A Recurrent Neural Network For Image Generation.txt │ │ ├── DeCAF.txt │ │ ├── Deep AutoRegressive Networks.txt │ │ ├── Deep Edge-Aware Filters.txt │ │ ├── Deep Generative Stochastic Networks Trainable by Backprop.txt │ │ ├── Deep Learning with Limited Numerical Precision.txt │ │ ├── Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction.txt │ │ ├── Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets.txt │ │ ├── Gated Feedback Recurrent Neural Networks.txt │ │ ├── Generative Moment Matching Networks.txt │ │ ├── Hierarchical Conditional Random Fields for Outlier Detection.txt │ │ ├── How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances.txt │ │ ├── Latent Semantic Representation Learning for Scene Classification.txt │ │ ├── Learning Ordered Representations with Nested Dropout.txt │ │ ├── Learning Program Embeddings to Propagate Feedback on Student Code.txt │ │ ├── Learning Transferable Features with Deep Adaptation Networks.txt │ │ ├── Learning by Stretching Deep Networks.txt │ │ ├── Learning to Disentangle Factors of Variation with Manifold Interaction.txt │ │ ├── MADE Masked Autoencoder for Distribution Estimation.txt │ │ ├── Marginalized Denoising Auto-encoders for Nonlinear Representations.txt │ │ ├── Modeling Order in Neural Word Embeddings at Scale.txt │ │ ├── Neural Variational Inference and Learning in Belief Networks.txt │ │ ├── On Deep Multi-View Representation Learning.txt │ │ ├── Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network.txt │ │ ├── Optimizing Neural Networks with Kronecker-factored Approximate Curvature.txt │ │ ├── Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.txt │ │ ├── Recurrent Convolutional Neural Networks for Scene Labeling.txt │ │ ├── Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix.txt │ │ ├── Show Attend and Tell Neural Image Caption Generation with Visual Attention.txt │ │ ├── Signal recovery from Pooling Representations.txt │ │ ├── Stable and Efficient Representation Learning with Nonnegativity Constraints.txt │ │ ├── Stochastic Backpropagation and Approximate Inference in Deep Generative Models.txt │ │ ├── Structured Recurrent Temporal Restricted Boltzmann Machines.txt │ │ ├── Unsupervised Domain Adaptation by Backpropagation.txt │ │ ├── Unsupervised Learning of Video Representations using LSTMs.txt │ │ ├── Variational Generative Stochastic Networks with Collaborative Shaping.txt │ │ ├── Weight Uncertainty in Neural Network.txt │ │ └── desktop.ini │ ├── 5. Ensemble and Crowdsourcing │ │ ├── A Convergence Rate Analysis for LogitBoost, MART and Their Variant.txt │ │ ├── Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy.txt │ │ ├── Boosting with Online Binary Learners for the Multiclass Bandit Problem.txt │ │ ├── Deep Boosting.txt │ │ ├── Dynamic Programming Boosting for Discriminative Macro-Action Discovery.txt │ │ ├── Ensemble Methods for Structured Prediction.txt │ │ ├── Ensemble-Based Tracking.txt │ │ ├── Gaussian Process Classification and Active Learning with Multiple Annotators.txt │ │ ├── Latent Confusion Analysis by Normalized Gamma Construction.txt │ │ ├── Narrowing the Gap.txt │ │ ├── Near-Optimally Teaching the Crowd to Classify.txt │ │ └── desktop.ini │ ├── 6. Feature Learning │ │ ├── A Unified Framework for Outlier-Robust PCA-like Algorithms.txt │ │ ├── An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy.txt │ │ ├── Circulant Binary Embedding.txt │ │ ├── Coding for Random Projections.txt │ │ ├── Compact Random Feature Maps.txt │ │ ├── Composite Quantization for Approximate Nearest Neighbor Search.txt │ │ ├── Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search.txt │ │ ├── Discriminative Features via Generalized Eigenvectors.txt │ │ ├── Efficient Learning of Mahalanobis Metrics for Ranking.txt │ │ ├── Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.txt │ │ ├── Factorized Point Process Intensities.txt │ │ ├── Large-Margin Metric Learning for Constrained Partitioning Problems.txt │ │ ├── Large-margin Weakly Supervised Dimensionality Reduction.txt │ │ ├── Low Rank Approximation using Error Correcting Coding Matrices.txt │ │ ├── Making Fisher Discriminant Analysis Scalable.txt │ │ ├── Memory and Computation Efficient PCA via Very Sparse Random Projections.txt │ │ ├── Multivariate Maximal Correlation Analysis.txt │ │ ├── Multiview Triplet Embedding Learning Attributes in Multiple Maps.txt │ │ ├── Nearest Neighbors Using Compact Sparse Codes.txt │ │ ├── Optimal Mean Robust Principal Component Analysis.txt │ │ ├── Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA.txt │ │ ├── Randomized Nonlinear Component Analysis.txt │ │ ├── Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization.txt │ │ ├── Robust Inverse Covariance Estimation under Noisy Measurements.txt │ │ ├── Robust Principal Component Analysis with Complex Noise.txt │ │ ├── Sparse meta-Gaussian information bottleneck.txt │ │ ├── Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares.txt │ │ ├── Stochastic Neighbor Compression.txt │ │ ├── Streaming Sparse Principal Component Analysis.txt │ │ └── desktop.ini │ ├── 7. Kernel Methods │ │ ├── A Divide-and-Conquer Solver for Kernel Support Vector Machines.txt │ │ ├── A Kernel Independence Test for Random Processes.txt │ │ ├── A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data.txt │ │ ├── A Unifying View of Representer Theorems.txt │ │ ├── A low variance consistent test of relative dependency.txt │ │ ├── Controversy in mechanistic modelling with Gaussian processes.txt │ │ ├── Distributed Gaussian Processes.txt │ │ ├── Double Nystrm Method An Efficient and Accurate Nystrm Scheme for Large-Scale Data Sets.txt │ │ ├── Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function.txt │ │ ├── Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr ULISSE.txt │ │ ├── Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods.txt │ │ ├── Finding Galaxies in the Shadows of Quasars with Gaussian Processes.txt │ │ ├── Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs.txt │ │ ├── Kernel Interpolation for Scalable Structured Gaussian Processes KISS-GP.txt │ │ ├── Kernel Mean Estimation and Stein Effect.txt │ │ ├── Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data.txt │ │ ├── Maximum Mean Discrepancy for Class Ratio Estimation.txt │ │ ├── Memory Efficient Kernel Approximation.txt │ │ ├── Nonparametric Estimation of Multi-View Latent Variable Models.txt │ │ ├── On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection.txt │ │ ├── Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.txt │ │ ├── Robust and Efficient Kernel Hyperparameter Paths with Guarantees.txt │ │ ├── Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes.txt │ │ ├── Sparse Variational Inference for Generalized GP Models.txt │ │ ├── The Kendall and Mallows Kernels for Permutations.txt │ │ ├── Variational Inference for Gaussian Process Modulated Poisson Processes.txt │ │ └── desktop.ini │ ├── 8. Online Learning │ │ ├── A Bayesian Framework for Online Classifier Ensemble.txt │ │ ├── A Physics-Based Model Prior for Object-Oriented MDPs.txt │ │ ├── Adaptive Belief Propagation.txt │ │ ├── Adaptive Monte Carlo via Bandit Allocation.txt │ │ ├── Adaptivity and Optimism.txt │ │ ├── An Online Learning Algorithm for Bilinear Models.txt │ │ ├── Cascading Bandits Learning to Rank in the Cascade Model.txt │ │ ├── Cheap Bandits.txt │ │ ├── Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications.txt │ │ ├── Covering Number for Efficient Heuristic-based POMDP Planning.txt │ │ ├── Efficient Learning in Large-Scale Combinatorial Semi-Bandits.txt │ │ ├── Following the Perturbed Leader for Online Structured Learning.txt │ │ ├── Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques.txt │ │ ├── Latent Bandits.txt │ │ ├── Learning Complex Neural Network Policies with Trajectory Optimization.txt │ │ ├── Linear Programming for Large-Scale Markov Decision Problems.txt │ │ ├── On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments.txt │ │ ├── On the convergence of no-regret learning in selfish routing.txt │ │ ├── One Practical Algorithm for Both Stochastic and Adversarial Bandits.txt │ │ ├── Online Clustering of Bandits.txt │ │ ├── Online Learning in Markov Decision Processes with Changing Cost Sequences.txt │ │ ├── Online Multi-Task Learning for Policy Gradient Methods.txt │ │ ├── Online Stochastic Optimization under Correlated Bandit Feedback.txt │ │ ├── Online Time Series Prediction with Missing Data.txt │ │ ├── Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing.txt │ │ ├── Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays.txt │ │ ├── Prediction with Limited Advice and Multiarmed Bandits with Paid Observations.txt │ │ ├── Preference-Based Rank Elicitation using Statistical Models.txt │ │ ├── Pursuit-Evasion Without Regret, with an Application to Trading.txt │ │ ├── Qualitative Multi-Armed Bandits A Quantile-Based Approach.txt │ │ ├── Reducing Dueling Bandits to Cardinal Bandits.txt │ │ ├── Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem.txt │ │ ├── Simple regret for infinitely many armed bandits.txt │ │ ├── Sparse Reinforcement Learning via Convex Optimization.txt │ │ ├── Spectral Bandits for Smooth Graph Functions.txt │ │ ├── Statistical analysis of stochastic gradient methods for generalized linear models.txt │ │ ├── Strongly Adaptive Online Learning.txt │ │ ├── Taming the Monster.txt │ │ ├── The Hedge Algorithm on a Continuum.txt │ │ ├── Thompson Sampling for Complex Online Problems.txt │ │ ├── Tracking Adversarial Targets.txt │ │ ├── Unimodal Bandits.txt │ │ └── desktop.ini │ └── 9. Optimization │ │ ├── A Deep Semi-NMF Model for Learning Hidden Representations.txt │ │ ├── A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data.txt │ │ ├── A General Analysis of the Convergence of ADMM.txt │ │ ├── A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models.txt │ │ ├── A Lower Bound for the Optimization of Finite Sums.txt │ │ ├── A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis.txt │ │ ├── A Nearly-Linear Time Framework for Graph-Structured Sparsity.txt │ │ ├── A Statistical Perspective on Algorithmic Leveraging.txt │ │ ├── A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate.txt │ │ ├── A Unified Framework for Consistency of Regularized Loss Minimizers.txt │ │ ├── Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization.txt │ │ ├── Adaptive Stochastic Alternating Direction Method of Multipliers.txt │ │ ├── Adding vs Averaging in Distributed Primal-Dual Optimization.txt │ │ ├── Adding vs. Averaging in Distributed Primal-Dual Optimization.txt │ │ ├── An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization.txt │ │ ├── An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization.txt │ │ ├── An Asynchronous Parallel Stochastic Coordinate Descent Algorithm.txt │ │ ├── An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection.txt │ │ ├── Asynchronous Distributed ADMM for Consensus Optimization.txt │ │ ├── Blitz A Principled Meta-Algorithm for Scaling Sparse Optimization.txt │ │ ├── Buffer k-d Trees.txt │ │ ├── CUR Algorithm for Partially Observed Matrices.txt │ │ ├── Cold-start Active Learning with Robust Ordinal Matrix Factorization.txt │ │ ├── Communication-Efficient Distributed Optimization using an Approximate Newton-type Method.txt │ │ ├── Complete Dictionary Recovery Using Nonconvex Optimization.txt │ │ ├── Convex Total Least Squares.txt │ │ ├── Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection.txt │ │ ├── Coresets for Nonparametric Estimation the Case of DP-Means.txt │ │ ├── Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising.txt │ │ ├── DiSCO Distributed Optimization for Self-Concordant Empirical Loss.txt │ │ ├── Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM.txt │ │ ├── Dual Query.txt │ │ ├── Efficient Algorithms for Robust One-bit Compressive Sensing.txt │ │ ├── Elementary Estimators for High-Dimensional Linear Regression.txt │ │ ├── Fast Multi-stage Submodular Maximization.txt │ │ ├── Fast Stochastic Alternating Direction Method of Multipliers.txt │ │ ├── Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods.txt │ │ ├── Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets.txt │ │ ├── Faster cover trees.txt │ │ ├── Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis.txt │ │ ├── Finito.txt │ │ ├── Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint.txt │ │ ├── Geometric Conditions for Subspace-Sparse Recovery.txt │ │ ├── Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems.txt │ │ ├── Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization.txt │ │ ├── Guaranteed Tensor Decomposition A Moment Approach.txt │ │ ├── Intersecting Faces Non-negative Matrix Factorization With New Guarantees.txt │ │ ├── K-hyperplane Hinge-Minimax Classifier.txt │ │ ├── Large-scale Multi-label Learning with Missing Labels.txt │ │ ├── Large-scale log-determinant computation through stochastic Chebyshev expansions.txt │ │ ├── Least Squares Revisited.txt │ │ ├── Linear Time Solver for Primal SVM.txt │ │ ├── Low-Rank Matrix Recovery from Row-and-Column Affine Measurements.txt │ │ ├── Making the Most of Bag of Words.txt │ │ ├── Margins, Kernels and Non-linear Smoothed Perceptrons.txt │ │ ├── Mind the duality gap safer rules for the Lasso.txt │ │ ├── Multi-label Classification via Feature-aware Implicit Label Space Encoding.txt │ │ ├── Multiresolution Matrix Factorization.txt │ │ ├── Nonlinear Information-Theoretic Compressive Measurement Design.txt │ │ ├── Nuclear Norm Minimization via Active Subspace Selection.txt │ │ ├── On Greedy Maximization of Entropy.txt │ │ ├── Online Learning of Eigenvectors.txt │ │ ├── Optimal Budget Allocation.txt │ │ ├── Outlier Path.txt │ │ ├── PASSCoDe Parallel ASynchronous Stochastic dual Co-ordinate Descent.txt │ │ ├── PU Learning for Matrix Completion.txt │ │ ├── Probabilistic Matrix Factorization with Non-random Missing Data.txt │ │ ├── Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions.txt │ │ ├── Rank-One Matrix Pursuit for Matrix Completion.txt │ │ ├── Riemannian Pursuit for Big Matrix Recovery.txt │ │ ├── Saddle Points and Accelerated Perceptron Algorithms.txt │ │ ├── Safe Screening with Variational Inequalities and Its Application to Lasso.txt │ │ ├── Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems.txt │ │ ├── Sample-based approximate regularization.txt │ │ ├── Scaling SVM and Least Absolute Deviations via Exact Data Reduction.txt │ │ ├── Sparse Subspace Clustering with Missing Entries.txt │ │ ├── Statistical-Computational Phase Transitions in Planted Models.txt │ │ ├── Stay on path PCA along graph paths.txt │ │ ├── Stochastic Dual Coordinate Ascent with Adaptive Probabilities.txt │ │ ├── Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers.txt │ │ ├── Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.txt │ │ ├── Stochastic Optimization with Importance Sampling for Regularized Loss Minimization.txt │ │ ├── Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization.txt │ │ ├── Structured Low-Rank Matrix Factorization.txt │ │ ├── Submodularity in Data Subset Selection and Active Learning.txt │ │ ├── Swept Approximate Message Passing for Sparse Estimation.txt │ │ ├── The Power of Randomization Distributed Submodular Maximization on Massive Datasets.txt │ │ ├── Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing.txt │ │ ├── Towards an optimal stochastic alternating direction method of multipliers.txt │ │ ├── Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional Hyper- Parameter Domains.txt │ │ ├── Un-regularizing approximate proximal point and faster stochastic algorithms for empirical risk minimization.txt │ │ ├── desktop.ini │ │ └── ell_ 1 p -Norm Regularization Error Bounds and Convergence Rate Analysis of First-Order Methods.txt ├── SGD │ ├── covtype-testing.txt │ ├── covtype-training.txt │ ├── dataset1-a9a-testing.txt │ └── dataset1-a9a-training.txt └── StopWords │ └── english ├── projectutil.py ├── runAssignment1.py ├── runAssignment2.py ├── runAssignment3.py ├── runAssignment4.py ├── runAssignment5.py └── tests ├── EnglishTextDataProcessingTest.py ├── ReductionTest.py └── __init__.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | .idea/ 3 | data/SantanderProductRecommendation/*.* 4 | -------------------------------------------------------------------------------- /.idea/.name: -------------------------------------------------------------------------------- 1 | dataminingcource -------------------------------------------------------------------------------- /.idea/data-mining-course.iml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/data-mining-course.iml -------------------------------------------------------------------------------- /.idea/dataminingcource.iml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/dataminingcource.iml -------------------------------------------------------------------------------- /.idea/encodings.xml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/encodings.xml -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/misc.xml -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/modules.xml -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/vcs.xml -------------------------------------------------------------------------------- /.idea/workspace.xml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/.idea/workspace.xml -------------------------------------------------------------------------------- /DataMingAssignments@NJU-CS.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/DataMingAssignments@NJU-CS.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/README.md -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/__init__.py -------------------------------------------------------------------------------- /assignment1/Assignment1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/Assignment1.pdf -------------------------------------------------------------------------------- /assignment1/EnglishTextDataProcessing/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /assignment1/EnglishTextDataProcessing/dataset.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/EnglishTextDataProcessing/dataset.py -------------------------------------------------------------------------------- /assignment1/EnglishTextDataProcessing/main.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/EnglishTextDataProcessing/main.py -------------------------------------------------------------------------------- /assignment1/EnglishTextDataProcessing/preprocess.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/EnglishTextDataProcessing/preprocess.py -------------------------------------------------------------------------------- /assignment1/EnglishTextDataProcessing/tfidf.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/EnglishTextDataProcessing/tfidf.py -------------------------------------------------------------------------------- /assignment1/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/README.txt -------------------------------------------------------------------------------- /assignment1/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/__init__.py -------------------------------------------------------------------------------- /assignment1/report.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/report.doc -------------------------------------------------------------------------------- /assignment1/report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/report.pdf -------------------------------------------------------------------------------- /assignment1/result/1. Active Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/1. Active Learning -------------------------------------------------------------------------------- /assignment1/result/10. Ranking: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/10. Ranking -------------------------------------------------------------------------------- /assignment1/result/11. Reinforcement Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/11. Reinforcement Learning -------------------------------------------------------------------------------- /assignment1/result/12. Supervised Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/12. Supervised Learning -------------------------------------------------------------------------------- /assignment1/result/13. Theory: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/13. Theory -------------------------------------------------------------------------------- /assignment1/result/14. Unsupervised and Semi-Supervised Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/14. Unsupervised and Semi-Supervised Learning -------------------------------------------------------------------------------- /assignment1/result/15. Others: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/15. Others -------------------------------------------------------------------------------- /assignment1/result/2. Applications: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/2. Applications -------------------------------------------------------------------------------- /assignment1/result/3. Bayesian Learning and Graphical Model: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/3. Bayesian Learning and Graphical Model -------------------------------------------------------------------------------- /assignment1/result/4. Deep Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/4. Deep Learning -------------------------------------------------------------------------------- /assignment1/result/5. Ensemble and Crowdsourcing: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/5. Ensemble and Crowdsourcing -------------------------------------------------------------------------------- /assignment1/result/6. Feature Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/6. Feature Learning -------------------------------------------------------------------------------- /assignment1/result/7. Kernel Methods: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/7. Kernel Methods -------------------------------------------------------------------------------- /assignment1/result/8. Online Learning: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/8. Online Learning -------------------------------------------------------------------------------- /assignment1/result/9. Optimization: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/9. Optimization -------------------------------------------------------------------------------- /assignment1/result/word_list.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment1/result/word_list.txt -------------------------------------------------------------------------------- /assignment2/Assignment2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/Assignment2.pdf -------------------------------------------------------------------------------- /assignment2/DimReductionwithSklearn.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/DimReductionwithSklearn.py -------------------------------------------------------------------------------- /assignment2/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/README.txt -------------------------------------------------------------------------------- /assignment2/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/__init__.py -------------------------------------------------------------------------------- /assignment2/isomap.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/isomap.py -------------------------------------------------------------------------------- /assignment2/knn.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/knn.py -------------------------------------------------------------------------------- /assignment2/main.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/main.py -------------------------------------------------------------------------------- /assignment2/pca.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/pca.py -------------------------------------------------------------------------------- /assignment2/report.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/report.doc -------------------------------------------------------------------------------- /assignment2/report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/report.pdf -------------------------------------------------------------------------------- /assignment2/result.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/result.txt -------------------------------------------------------------------------------- /assignment2/svd.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment2/svd.py -------------------------------------------------------------------------------- /assignment3/Assignment3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/Assignment3.pdf -------------------------------------------------------------------------------- /assignment3/Document clustering based on non-negative matrix factorization.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/Document clustering based on non-negative matrix factorization.pdf -------------------------------------------------------------------------------- /assignment3/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/README.txt -------------------------------------------------------------------------------- /assignment3/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/__init__.py -------------------------------------------------------------------------------- /assignment3/belkin01laplacian.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/belkin01laplacian.pdf -------------------------------------------------------------------------------- /assignment3/kmedoids.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/kmedoids.py -------------------------------------------------------------------------------- /assignment3/report.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/report.doc -------------------------------------------------------------------------------- /assignment3/report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/report.pdf -------------------------------------------------------------------------------- /assignment3/result.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/result.txt -------------------------------------------------------------------------------- /assignment3/run.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/run.py -------------------------------------------------------------------------------- /assignment3/score.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/score.py -------------------------------------------------------------------------------- /assignment3/spectralclustering.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment3/spectralclustering.py -------------------------------------------------------------------------------- /assignment4/Assignment4.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/Assignment4.pdf -------------------------------------------------------------------------------- /assignment4/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/README.txt -------------------------------------------------------------------------------- /assignment4/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/__init__.py -------------------------------------------------------------------------------- /assignment4/figure/covtype_SGD-LogLossRegression-Error-Rate_错误率.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/covtype_SGD-LogLossRegression-Error-Rate_错误率.png -------------------------------------------------------------------------------- /assignment4/figure/covtype_SGD-LogLossRegression-Objective-Function_目标函数.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/covtype_SGD-LogLossRegression-Objective-Function_目标函数.png -------------------------------------------------------------------------------- /assignment4/figure/covtype_SGD-RidgeRegression-Error-Rate_错误率.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/covtype_SGD-RidgeRegression-Error-Rate_错误率.png -------------------------------------------------------------------------------- /assignment4/figure/covtype_SGD-RidgeRegression-Objective-Function_目标函数.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/covtype_SGD-RidgeRegression-Objective-Function_目标函数.png -------------------------------------------------------------------------------- /assignment4/figure/dataset1-a9a_SGD-LogLossRegression-Error-Rate_错误率.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/dataset1-a9a_SGD-LogLossRegression-Error-Rate_错误率.png -------------------------------------------------------------------------------- /assignment4/figure/dataset1-a9a_SGD-LogLossRegression-Objective-Function_目标函数.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/dataset1-a9a_SGD-LogLossRegression-Objective-Function_目标函数.png -------------------------------------------------------------------------------- /assignment4/figure/dataset1-a9a_SGD-RidgeRegression-Error-Rate_错误率.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/dataset1-a9a_SGD-RidgeRegression-Error-Rate_错误率.png -------------------------------------------------------------------------------- /assignment4/figure/dataset1-a9a_SGD-RidgeRegression-Objective-Function_目标函数.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/figure/dataset1-a9a_SGD-RidgeRegression-Objective-Function_目标函数.png -------------------------------------------------------------------------------- /assignment4/logisticregression.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/logisticregression.py -------------------------------------------------------------------------------- /assignment4/main.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/main.py -------------------------------------------------------------------------------- /assignment4/plotfigure.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/plotfigure.py -------------------------------------------------------------------------------- /assignment4/report.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/report.doc -------------------------------------------------------------------------------- /assignment4/report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/report.pdf -------------------------------------------------------------------------------- /assignment4/result.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/result.txt -------------------------------------------------------------------------------- /assignment4/ridgeregression.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/ridgeregression.py -------------------------------------------------------------------------------- /assignment4/sgdwithsklearn.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment4/sgdwithsklearn.py -------------------------------------------------------------------------------- /assignment5/Assignment5.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/Assignment5.pdf -------------------------------------------------------------------------------- /assignment5/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/README.txt -------------------------------------------------------------------------------- /assignment5/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/__init__.py -------------------------------------------------------------------------------- /assignment5/adaboost.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/adaboost.py -------------------------------------------------------------------------------- /assignment5/kfolddataset.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/kfolddataset.py -------------------------------------------------------------------------------- /assignment5/main.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/main.py -------------------------------------------------------------------------------- /assignment5/naivebayes.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/naivebayes.py -------------------------------------------------------------------------------- /assignment5/report.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/report.doc -------------------------------------------------------------------------------- /assignment5/report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment5/report.pdf -------------------------------------------------------------------------------- /assignment6/Assignment6.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/Assignment6.pdf -------------------------------------------------------------------------------- /assignment6/Data - Santander Product Recommendation _ Kaggle.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/Data - Santander Product Recommendation _ Kaggle.pdf -------------------------------------------------------------------------------- /assignment6/README.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/README.txt -------------------------------------------------------------------------------- /assignment6/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/__init__.py -------------------------------------------------------------------------------- /assignment6/report.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/report.doc -------------------------------------------------------------------------------- /assignment6/report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/report.pdf -------------------------------------------------------------------------------- /assignment6/santander.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/assignment6/santander.py -------------------------------------------------------------------------------- /data/BinaryDatasets/sonar-test.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/BinaryDatasets/sonar-test.txt -------------------------------------------------------------------------------- /data/BinaryDatasets/sonar-train.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/BinaryDatasets/sonar-train.txt -------------------------------------------------------------------------------- /data/BinaryDatasets/splice-test.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/BinaryDatasets/splice-test.txt -------------------------------------------------------------------------------- /data/BinaryDatasets/splice-train.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/BinaryDatasets/splice-train.txt -------------------------------------------------------------------------------- /data/Clustering/german.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/Clustering/german.txt -------------------------------------------------------------------------------- /data/Clustering/mnist.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/Clustering/mnist.txt -------------------------------------------------------------------------------- /data/EnsembleLearning/breast-cancer-assignment5.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/EnsembleLearning/breast-cancer-assignment5.txt -------------------------------------------------------------------------------- /data/EnsembleLearning/german-assignment5.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/EnsembleLearning/german-assignment5.txt -------------------------------------------------------------------------------- /data/ICML/1. Active Learning/Active Transfer Learning under Model Shift.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/1. Active Learning/Active Transfer Learning under Model Shift.txt -------------------------------------------------------------------------------- /data/ICML/1. Active Learning/Diagnosis determination.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/1. Active Learning/Diagnosis determination.txt -------------------------------------------------------------------------------- /data/ICML/1. Active Learning/Gaussian Process Optimization with Mutual Information.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/1. Active Learning/Gaussian Process Optimization with Mutual Information.txt -------------------------------------------------------------------------------- /data/ICML/1. Active Learning/Hard-Margin Active Linear Regression.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/1. Active Learning/Hard-Margin Active Linear Regression.txt -------------------------------------------------------------------------------- /data/ICML/1. Active Learning/Nonmyopic $epsilon$-Bayes-Optimal Active Learning of Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/1. Active Learning/Nonmyopic $epsilon$-Bayes-Optimal Active Learning of Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/A Consistent Histogram Estimator for Exchangeable Graph Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/A Consistent Histogram Estimator for Exchangeable Graph Models.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Coherent Matrix Completion.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Coherent Matrix Completion.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Distributional Rank Aggregation and an Axiomatic Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Distributional Rank Aggregation and an Axiomatic Analysis.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Exponential Family Matrix Completion under Structural Constraints.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Exponential Family Matrix Completion under Structural Constraints.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Generalization error bounds for learning to rank Does the length of document lists matter.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Generalization error bounds for learning to rank Does the length of document lists matter.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/MRA-based Statistical Learning from Incomplete Rankings.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/MRA-based Statistical Learning from Incomplete Rankings.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Near-Optimal Joint Object Matching via Convex Relaxation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Near-Optimal Joint Object Matching via Convex Relaxation.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Preference Completion Large-scale Collaborative Ranking from Pairwise Comparisons.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Preference Completion Large-scale Collaborative Ranking from Pairwise Comparisons.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Spectral MLE Top-K Rank Aggregation from Pairwise Comparisons.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Spectral MLE Top-K Rank Aggregation from Pairwise Comparisons.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Square Deal.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Square Deal.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/Universal Matrix Completion.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/Universal Matrix Completion.txt -------------------------------------------------------------------------------- /data/ICML/10. Ranking/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/10. Ranking/desktop.ini -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/A Deeper Look at Planning as Learning from Replay.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/A Deeper Look at Planning as Learning from Replay.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/A new Q(lambda) with interim forward view and Monte Carlo equivalence.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/A new Q(lambda) with interim forward view and Monte Carlo equivalence.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Abstraction Selection in Model-based Reinforcement Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Abstraction Selection in Model-based Reinforcement Learning.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Active Learning of Parameterized Skills.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Active Learning of Parameterized Skills.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Approximate Policy Iteration Schemes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Approximate Policy Iteration Schemes.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Bias in Natural Actor-Critic Algorithms.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Bias in Natural Actor-Critic Algorithms.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Deterministic Policy Gradient Algorithms.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Deterministic Policy Gradient Algorithms.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Fictitious Self-Play in Extensive-Form Games.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Fictitious Self-Play in Extensive-Form Games.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/GeNGA.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/GeNGA.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/High Confidence Policy Improvement.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/High Confidence Policy Improvement.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Model-Based Relational RL When Object Existence is Partially Observable.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Model-Based Relational RL When Object Existence is Partially Observable.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Non-Stationary Approximate Modified Policy Iteration.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Non-Stationary Approximate Modified Policy Iteration.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Off-policy Model-based Learning under Unknown Factored Dynamics.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Off-policy Model-based Learning under Unknown Factored Dynamics.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/On the Rate of Convergence and Error Bounds for LSTD lambda.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/On the Rate of Convergence and Error Bounds for LSTD lambda.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/PAC-inspired Option Discovery in Lifelong Reinforcement Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/PAC-inspired Option Discovery in Lifelong Reinforcement Learning.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Programming by Feedback.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Programming by Feedback.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Robust partially observable Markov decision process.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Robust partially observable Markov decision process.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Scaling Up Robust MDPs using Function Approximation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Scaling Up Robust MDPs using Function Approximation.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Time-Regularized Interrupting Options (TRIO).txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Time-Regularized Interrupting Options (TRIO).txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Training Deep Convolutional Neural Networks to Play Go.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Training Deep Convolutional Neural Networks to Play Go.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/True Online TD(lambda).txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/True Online TD(lambda).txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Trust Region Policy Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Trust Region Policy Optimization.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/Universal Value Function Approximators.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/Universal Value Function Approximators.txt -------------------------------------------------------------------------------- /data/ICML/11. Reinforcement Learning/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/11. Reinforcement Learning/desktop.ini -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/A New Generalized Error Path Algorithm for Model Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/A New Generalized Error Path Algorithm for Model Selection.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Attribute Efficient Linear Regression with Distribution-Dependent Sampling.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Attribute Efficient Linear Regression with Distribution-Dependent Sampling.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Condensed Filter Tree for Cost-Sensitive Multi-Label Classification.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Condensed Filter Tree for Cost-Sensitive Multi-Label Classification.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Consistent Multiclass Algorithms for Complex Performance Measures.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Consistent Multiclass Algorithms for Complex Performance Measures.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Dynamic Sensing Better Classification under Acquisition Constraints.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Dynamic Sensing Better Classification under Acquisition Constraints.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Feature-Budgeted Random Forest.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Feature-Budgeted Random Forest.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Learning from Corrupted Binary Labels via Class-Probability Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Learning from Corrupted Binary Labels via Class-Probability Estimation.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Multi-instance multi-label learning in the presence of novel class instances.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Multi-instance multi-label learning in the presence of novel class instances.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Robust Learning under Uncertain Test Distributions.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Robust Learning under Uncertain Test Distributions.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/Support Matrix Machines.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/Support Matrix Machines.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/The Coherent Loss Function for Classification.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/The Coherent Loss Function for Classification.txt -------------------------------------------------------------------------------- /data/ICML/12. Supervised Learning/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/12. Supervised Learning/desktop.ini -------------------------------------------------------------------------------- /data/ICML/13. Theory/(Near) Dimension Independent Risk Bounds for Differentially Private Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/(Near) Dimension Independent Risk Bounds for Differentially Private Learning.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/A Theoretical Analysis of Metric Hypothesis Transfer Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/A Theoretical Analysis of Metric Hypothesis Transfer Learning.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Anti-differentiating approximation algorithms.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Anti-differentiating approximation algorithms.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Classification with Low Rank and Missing Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Classification with Low Rank and Missing Data.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Concentration in unbounded metric spaces and algorithmic stability.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Concentration in unbounded metric spaces and algorithmic stability.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Convergence rate of Bayesian tensor estimator and its minimax optimality.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Convergence rate of Bayesian tensor estimator and its minimax optimality.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Convex Calibrated Surrogates for Hierarchical Classification.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Convex Calibrated Surrogates for Hierarchical Classification.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Deterministic Independent Component Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Deterministic Independent Component Analysis.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Distributed Estimation of Generalized Matrix Rank Efficient Algorithms and Lower Bounds.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Distributed Estimation of Generalized Matrix Rank Efficient Algorithms and Lower Bounds.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Entropy-Based Concentration Inequalities for Dependent Variables.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Entropy-Based Concentration Inequalities for Dependent Variables.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Finding Dense Subgraphs via Low-Rank Bilinear Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Finding Dense Subgraphs via Low-Rank Bilinear Optimization.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Heavy-tailed regression with a generalized median-of-means.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Heavy-tailed regression with a generalized median-of-means.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/K-means recovers ICA filters when independent components are sparse.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/K-means recovers ICA filters when independent components are sparse.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Learnability of the Superset Label Learning Problem.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Learnability of the Superset Label Learning Problem.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Learning Polynomials with Neural Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Learning Polynomials with Neural Networks.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Learning Theory and Algorithms for revenue optimization in second price auctions with reserve.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Learning Theory and Algorithms for revenue optimization in second price auctions with reserve.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Maximum Margin Multiclass Nearest Neighbors.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Maximum Margin Multiclass Nearest Neighbors.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Multi-period Trading Prediction Markets with Connections to Machine Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Multi-period Trading Prediction Markets with Connections to Machine Learning.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Nonnegative Sparse PCA with Provable Guarantees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Nonnegative Sparse PCA with Provable Guarantees.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Optimizing Non-decomposable Performance Measures A Tale of Two Classes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Optimizing Non-decomposable Performance Measures A Tale of Two Classes.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Provable Bounds for Learning Some Deep Representations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Provable Bounds for Learning Some Deep Representations.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Risk and Regret of Hierarchical Bayesian Learners.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Risk and Regret of Hierarchical Bayesian Learners.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Sample Efficient Reinforcement Learning with Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Sample Efficient Reinforcement Learning with Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Scaling Up Approximate Value Iteration with Options.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Scaling Up Approximate Value Iteration with Options.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Surrogate Functions for Maximizing Precision at the Top.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Surrogate Functions for Maximizing Precision at the Top.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/The Ladder A Reliable Leaderboard for Machine Learning Competitions.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/The Ladder A Reliable Leaderboard for Machine Learning Competitions.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Theory of Dual-sparse Regularized Randomized Reduction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Theory of Dual-sparse Regularized Randomized Reduction.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/Towards Minimax Online Learning with Unknown Time Horizon.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/Towards Minimax Online Learning with Unknown Time Horizon.txt -------------------------------------------------------------------------------- /data/ICML/13. Theory/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/13. Theory/desktop.ini -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/A Convex Optimization Framework for Bi-Clustering.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/A Convex Optimization Framework for Bi-Clustering.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/A Probabilistic Model for Dirty Multi-task Feature Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/A Probabilistic Model for Dirty Multi-task Feature Selection.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Active Detection via Adaptive Submodularity.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Active Detection via Adaptive Submodularity.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Active Nearest Neighbors in Changing Environments.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Active Nearest Neighbors in Changing Environments.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/An Information Geometry of Statistical Manifold Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/An Information Geometry of Statistical Manifold Learning.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/An embarrassingly simple approach to zero-shot learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/An embarrassingly simple approach to zero-shot learning.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Anomaly Ranking as Supervised Bipartite Ranking.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Anomaly Ranking as Supervised Bipartite Ranking.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Asymmetric Transfer Learning with Deep Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Asymmetric Transfer Learning with Deep Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Binary Embedding Fundamental Limits and Fast Algorithm.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Binary Embedding Fundamental Limits and Fast Algorithm.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Clustering in the Presence of Background Noise.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Clustering in the Presence of Background Noise.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Context-based Unsupervised Data Fusion for Decision Making.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Context-based Unsupervised Data Fusion for Decision Making.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Convex Formulation for Learning from Positive and Unlabeled Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Convex Formulation for Learning from Positive and Unlabeled Data.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Convex Learning of Multiple Tasks and their Structure.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Convex Learning of Multiple Tasks and their Structure.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Deep Unsupervised Learning using Nonequilibrium Thermodynamics.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Deep Unsupervised Learning using Nonequilibrium Thermodynamics.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Demystifying Information-Theoretic Clustering.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Demystifying Information-Theoretic Clustering.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Entropic Graph-based Posterior Regularization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Entropic Graph-based Posterior Regularization.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Fast Computation of Wasserstein Barycenters.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Fast Computation of Wasserstein Barycenters.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Graph-based Semi-supervised Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Graph-based Semi-supervised Learning.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Hashing for Distributed Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Hashing for Distributed Data.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Hidden Markov Anomaly Detection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Hidden Markov Anomaly Detection.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Hierarchical Dirichlet Scaling Process.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Hierarchical Dirichlet Scaling Process.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Hierarchical Quasi-Clustering Methods for Asymmetric Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Hierarchical Quasi-Clustering Methods for Asymmetric Networks.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Information Geometry and Minimum Description Length Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Information Geometry and Minimum Description Length Networks.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Landmarking Manifolds with Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Landmarking Manifolds with Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Learning Local Invariant Mahalanobis Distances.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Learning Local Invariant Mahalanobis Distances.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Local Ordinal Embedding.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Local Ordinal Embedding.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Local algorithms for interactive clustering.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Local algorithms for interactive clustering.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Max-Margin Infinite Hidden Markov Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Max-Margin Infinite Hidden Markov Models.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Multi-Task Learning for Subspace Segmentation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Multi-Task Learning for Subspace Segmentation.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/On Symmetric and Asymmetric LSHs for Inner Product Search.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/On Symmetric and Asymmetric LSHs for Inner Product Search.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/On learning to localize objects with minimal supervision.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/On learning to localize objects with minimal supervision.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Online Bayesian Passive-Aggressive Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Online Bayesian Passive-Aggressive Learning.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Spectral Clustering via the Power Method Provably.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Spectral Clustering via the Power Method Provably.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Standardized Mutual Information for Clustering Comparisons.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Standardized Mutual Information for Clustering Comparisons.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Structural Maxent Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Structural Maxent Models.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Structured Generative Models of Natural Source Code.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Structured Generative Models of Natural Source Code.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Subsampling Methods for Persistent Homology.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Subsampling Methods for Persistent Homology.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/The f-Adjusted Graph Laplacian.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/The f-Adjusted Graph Laplacian.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Transductive Learning with Multi-class Volume Approximation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Transductive Learning with Multi-class Volume Approximation.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Two-Stage Metric Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Two-Stage Metric Learning.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Von Mises-Fisher Clustering Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Von Mises-Fisher Clustering Models.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/Wasserstein Propagation for Semi-Supervised Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/Wasserstein Propagation for Semi-Supervised Learning.txt -------------------------------------------------------------------------------- /data/ICML/14. Unsupervised and Semi-Supervised Learning/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/14. Unsupervised and Semi-Supervised Learning/desktop.ini -------------------------------------------------------------------------------- /data/ICML/15. Others/A Bayesian Wilcoxon signed-rank test based on the Dirichlet process.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/A Bayesian Wilcoxon signed-rank test based on the Dirichlet process.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Alternating Minimization for Mixed Linear Regression.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Alternating Minimization for Mixed Linear Regression.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Computing Parametric Ranking Models via Rank-Breaking.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Computing Parametric Ranking Models via Rank-Breaking.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Concept Drift Detection Through Resampling.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Concept Drift Detection Through Resampling.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Consistency of Causal Inference under the Additive Noise Model.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Consistency of Causal Inference under the Additive Noise Model.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Counterfactual Risk Minimization Learning from Logged Bandit Feedback.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Counterfactual Risk Minimization Learning from Logged Bandit Feedback.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Discovering Temporal Causal Relations from Subsampled Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Discovering Temporal Causal Relations from Subsampled Data.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Estimating Latent-Variable Graphical Models using Moments and Likelihoods.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Estimating Latent-Variable Graphical Models using Moments and Likelihoods.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Generalized Exponential Concentration Inequality for Renyi Divergence Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Generalized Exponential Concentration Inequality for Renyi Divergence Estimation.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/High Order Regularization for Semi-Supervised Learning of Structured Output Problems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/High Order Regularization for Semi-Supervised Learning of Structured Output Problems.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/How Hard is Inference for Structured Prediction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/How Hard is Inference for Structured Prediction.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Learning Deep Structured Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Learning Deep Structured Models.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Learning Fast-Mixing Models for Structured Prediction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Learning Fast-Mixing Models for Structured Prediction.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Learning Mixtures of Linear Classifiers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Learning Mixtures of Linear Classifiers.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Learning Scale-Free Networks by Dynamic Node Specific Degree Prior.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Learning Scale-Free Networks by Dynamic Node Specific Degree Prior.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Learning Submodular Losses with the Lovasz Hinge.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Learning Submodular Losses with the Lovasz Hinge.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Learning to Search Better than Your Teacher.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Learning to Search Better than Your Teacher.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Marginal Structured SVM with Hidden Variables.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Marginal Structured SVM with Hidden Variables.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Methods of Moments for Learning Stochastic Languages.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Methods of Moments for Learning Stochastic Languages.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/On Robustness and Regularization of Structural Support Vector Machines.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/On Robustness and Regularization of Structural Support Vector Machines.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Removing systematic errors for exoplanet search via latent causes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Removing systematic errors for exoplanet search via latent causes.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Spectral Regularization for Max-Margin Sequence Tagging.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Spectral Regularization for Max-Margin Sequence Tagging.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Structured Prediction of Network Response.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Structured Prediction of Network Response.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Telling cause from effect in deterministic linear dynamical systems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Telling cause from effect in deterministic linear dynamical systems.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/The Falling Factorial Basis and Its Statistical Applications.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/The Falling Factorial Basis and Its Statistical Applications.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/Towards a Learning Theory of Cause-Effect Inference.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/Towards a Learning Theory of Cause-Effect Inference.txt -------------------------------------------------------------------------------- /data/ICML/15. Others/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/15. Others/desktop.ini -------------------------------------------------------------------------------- /data/ICML/2. Applications/A Clockwork RNN.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/A Clockwork RNN.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/A Divide and Conquer Framework for Distributed Graph Clustering.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/A Divide and Conquer Framework for Distributed Graph Clustering.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/A Fast Variational Approach for Learning Markov Random Field Language Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/A Fast Variational Approach for Learning Markov Random Field Language Models.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/A Linear Dynamical System Model for Text.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/A Linear Dynamical System Model for Text.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/A Multitask Point Process Predictive Model.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/A Multitask Point Process Predictive Model.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/An Aligned Subtree Kernel for Weighted Graphs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/An Aligned Subtree Kernel for Weighted Graphs.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Approval Voting and Incentives in Crowdsourcing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Approval Voting and Incentives in Crowdsourcing.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Bayesian Multiple Target Localization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Bayesian Multiple Target Localization.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Bimodal Modelling of Source Code and Natural Language.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Bimodal Modelling of Source Code and Natural Language.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Boosting multi-step autoregressive forecasts.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Boosting multi-step autoregressive forecasts.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Budget Allocation Problem with Multiple Advertisers A Game Theoretic View.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Budget Allocation Problem with Multiple Advertisers A Game Theoretic View.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Community Detection Using Time-Dependent Personalized PageRank.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Community Detection Using Time-Dependent Personalized PageRank.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Compositional Morphology for Word Representations and Language Modelling.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Compositional Morphology for Word Representations and Language Modelling.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Consistent estimation of dynamic and multi-layer block models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Consistent estimation of dynamic and multi-layer block models.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Correlation Clustering in Data Streams.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Correlation Clustering in Data Streams.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Differentially Private Bayesian Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Differentially Private Bayesian Optimization.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Discovering Latent Network Structure in Point Process Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Discovering Latent Network Structure in Point Process Data.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Distributed Representations of Sentences and Documents.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Distributed Representations of Sentences and Documents.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Effective Bayesian Modeling of Groups of Related Count Time Series.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Effective Bayesian Modeling of Groups of Related Count Time Series.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Efficient Dimensionality Reduction for High-Dimensional Network Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Efficient Dimensionality Reduction for High-Dimensional Network Estimation.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Efficient Label Propagation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Efficient Label Propagation.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Estimating Diffusion Network Structures.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Estimating Diffusion Network Structures.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/From Word Embeddings To Document Distances.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/From Word Embeddings To Document Distances.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Functional Subspace Clustering with Application to Time Series.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Functional Subspace Clustering with Application to Time Series.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Global graph kernels using geometric embeddings.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Global graph kernels using geometric embeddings.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/HawkesTopic A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/HawkesTopic A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Inferring Graphs from Cascades A Sparse Recovery Framework.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Inferring Graphs from Cascades A Sparse Recovery Framework.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Influence Function Learning in Information Diffusion Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Influence Function Learning in Information Diffusion Networks.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Is Feature Selection Secure against Training Data Poisoning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Is Feature Selection Secure against Training Data Poisoning.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Joint Inference of Multiple Label Types in Large Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Joint Inference of Multiple Label Types in Large Networks.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Learning Character-level Representations for Part-of-Speech Tagging.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Learning Character-level Representations for Part-of-Speech Tagging.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Learning Graphs with a Few Hubs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Learning Graphs with a Few Hubs.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Learning Modular Structures from Network Data and Node Variables.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Learning Modular Structures from Network Data and Node Variables.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Learning Parametric-Output HMMs with Two Aliased States.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Learning Parametric-Output HMMs with Two Aliased States.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Learning Word Representations with Hierarchical Sparse Coding.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Learning Word Representations with Hierarchical Sparse Coding.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Learning from Contagion (Without Timestamps).txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Learning from Contagion (Without Timestamps).txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Long Short-Term Memory Over Recursive Structures.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Long Short-Term Memory Over Recursive Structures.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Modeling Correlated Arrival Events with Latent Semi-Markov Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Modeling Correlated Arrival Events with Latent Semi-Markov Processes.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Moderated and Drifting Linear Dynamical Systems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Moderated and Drifting Linear Dynamical Systems.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Multimodal Neural Language Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Multimodal Neural Language Models.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Phrase-based Image Captioning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Phrase-based Image Captioning.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Privacy for Free Posterior Sampling and Stochastic Gradient Monte Carlo.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Privacy for Free Posterior Sampling and Stochastic Gradient Monte Carlo.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Rademacher Observations Private Data and Boosting.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Rademacher Observations Private Data and Boosting.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Scalable Model Selection for Large-Scale Factorial Relational Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Scalable Model Selection for Large-Scale Factorial Relational Models.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Stochastic Variational Inference for Bayesian Time Series Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Stochastic Variational Inference for Bayesian Time Series Models.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/The Composition Theorem for Differential Privacy.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/The Composition Theorem for Differential Privacy.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Threshold Influence Model for Allocating Advertising Budgets.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Threshold Influence Model for Allocating Advertising Budgets.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Towards End-To-End Speech Recognition with Recurrent Neural Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Towards End-To-End Speech Recognition with Recurrent Neural Networks.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/Weighted Graph Clustering with Non-Uniform Uncertainties.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/Weighted Graph Clustering with Non-Uniform Uncertainties.txt -------------------------------------------------------------------------------- /data/ICML/2. Applications/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/2. Applications/desktop.ini -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/A Bayesian nonparametric procedure for comparing algorithms.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/A Bayesian nonparametric procedure for comparing algorithms.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/A Compilation Target for Probabilistic Programming Languages.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/A Compilation Target for Probabilistic Programming Languages.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/A Discriminative Latent Variable Model for Online Clustering.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/A Discriminative Latent Variable Model for Online Clustering.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/A PAC-Bayesian bound for Lifelong Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/A PAC-Bayesian bound for Lifelong Learning.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/A reversible infinite HMM using normalised random measures.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/A reversible infinite HMM using normalised random measures.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Admixture of Poisson MRFs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Admixture of Poisson MRFs.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Affinity Weighted Embedding.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Affinity Weighted Embedding.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Agnostic Bayesian Learning of Ensembles.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Agnostic Bayesian Learning of Ensembles.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/An Efficient Approach for Assessing Hyperparameter Importance.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/An Efficient Approach for Assessing Hyperparameter Importance.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Atomic Spatial Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Atomic Spatial Processes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Austerity in MCMC Land.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Austerity in MCMC Land.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Bayesian Max-margin Multi-Task Learning with Data Augmentation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Bayesian Max-margin Multi-Task Learning with Data Augmentation.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Bayesian Optimization with Inequality Constraints.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Bayesian Optimization with Inequality Constraints.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Bayesian and Empirical Bayesian Forests.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Bayesian and Empirical Bayesian Forests.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Beta Diffusion Trees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Beta Diffusion Trees.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Dealing with small data On the generalization of context trees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Dealing with small data On the generalization of context trees.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Discrete Chebyshev Classifiers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Discrete Chebyshev Classifiers.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Distributed Inference for Dirichlet Process Mixture Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Distributed Inference for Dirichlet Process Mixture Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Distributed Stochastic Gradient MCMC.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Distributed Stochastic Gradient MCMC.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Doubly Stochastic Variational Bayes for non-Conjugate Inference.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Doubly Stochastic Variational Bayes for non-Conjugate Inference.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Efficient Continuous-Time Markov Chain Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Efficient Continuous-Time Markov Chain Estimation.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Efficient Training of LDA on a GPU by Mean-for-Mode Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Efficient Training of LDA on a GPU by Mean-for-Mode Estimation.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Exchangeable Variable Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Exchangeable Variable Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Exponential Integration for Hamiltonian Monte Carlo.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Exponential Integration for Hamiltonian Monte Carlo.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Fast Allocation of Gaussian Process Experts.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Fast Allocation of Gaussian Process Experts.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Filtering with Abstract Particles.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Filtering with Abstract Particles.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Fixed-point algorithms for learning determinantal point processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Fixed-point algorithms for learning determinantal point processes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Gaussian Approximation of Collective Graphical Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Gaussian Approximation of Collective Graphical Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Hamiltonian Monte Carlo Without Detailed Balance.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Hamiltonian Monte Carlo Without Detailed Balance.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Harmonic Exponential Families on Manifolds.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Harmonic Exponential Families on Manifolds.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Inferning with High Girth Graphical Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Inferning with High Girth Graphical Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Input Warping for Bayesian Optimization of Non-Stationary Functions.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Input Warping for Bayesian Optimization of Non-Stationary Functions.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/JUMP-Means Small-Variance Asymptotics for Markov Jump Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/JUMP-Means Small-Variance Asymptotics for Markov Jump Processes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Kernel Adaptive Metropolis-Hastings.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Kernel Adaptive Metropolis-Hastings.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Large-scale Distributed Dependent Nonparametric Trees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Large-scale Distributed Dependent Nonparametric Trees.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Learning Latent Variable Gaussian Graphical Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Learning Latent Variable Gaussian Graphical Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Learning the Irreducible Representations of Commutative Lie Groups.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Learning the Irreducible Representations of Commutative Lie Groups.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Learning the Parameters of Determinantal Point Process Kernels.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Learning the Parameters of Determinantal Point Process Kernels.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Linear and Parallel Learning of Markov Random Fields.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Linear and Parallel Learning of Markov Random Fields.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Manifold-valued Dirichlet Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Manifold-valued Dirichlet Processes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Markov Mixed Membership Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Markov Mixed Membership Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Memory (and Time) Efficient Sequential Monte Carlo.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Memory (and Time) Efficient Sequential Monte Carlo.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Message Passing for Collective Graphical Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Message Passing for Collective Graphical Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Metadata Dependent Mondrian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Metadata Dependent Mondrian Processes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Min-Max Problems on Factor Graphs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Min-Max Problems on Factor Graphs.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Nested Sequential Monte Carlo Methods.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Nested Sequential Monte Carlo Methods.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Nonparametric Estimation of Renyi Divergence and Friends.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Nonparametric Estimation of Renyi Divergence and Friends.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/On Modelling Non-linear Topical Dependencies.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/On Modelling Non-linear Topical Dependencies.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Ordinal Mixed Membership Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Ordinal Mixed Membership Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Pitfalls in the use of Parallel Inference for the Dirichlet Process.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Pitfalls in the use of Parallel Inference for the Dirichlet Process.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Preserving Modes and Messages via Diverse Particle Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Preserving Modes and Messages via Diverse Particle Selection.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Probabilistic Partial Canonical Correlation Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Probabilistic Partial Canonical Correlation Analysis.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Putting MRFs on a Tensor Train.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Putting MRFs on a Tensor Train.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Rectangular Tiling Process.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Rectangular Tiling Process.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Reified Context Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Reified Context Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Robust RegBayes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Robust RegBayes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Safe Exploration for Optimization with Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Safe Exploration for Optimization with Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Scalable Bayesian Optimization Using Deep Neural Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Scalable Bayesian Optimization Using Deep Neural Networks.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Scalable Deep Poisson Factor Analysis for Topic Modeling.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Scalable Deep Poisson Factor Analysis for Topic Modeling.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Scalable Semidefinite Relaxation for Maximum A Posterior Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Scalable Semidefinite Relaxation for Maximum A Posterior Estimation.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Scalable Variational Inference in Log-supermodular Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Scalable Variational Inference in Log-supermodular Models.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Scalable and Robust Bayesian Inference via the Median Posterior.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Scalable and Robust Bayesian Inference via the Median Posterior.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Skip Context Tree Switching.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Skip Context Tree Switching.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Stochastic Gradient Hamiltonian Monte Carlo.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Stochastic Gradient Hamiltonian Monte Carlo.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/The Benefits of Learning with Strongly Convex Approximate Inference.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/The Benefits of Learning with Strongly Convex Approximate Inference.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/The Inverse Regression Topic Model.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/The Inverse Regression Topic Model.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Towards scaling up Markov chain Monte Carlo.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Towards scaling up Markov chain Monte Carlo.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Variational Inference with Normalizing Flows.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Variational Inference with Normalizing Flows.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/Vector-Space Markov Random Fields via Exponential Families.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/Vector-Space Markov Random Fields via Exponential Families.txt -------------------------------------------------------------------------------- /data/ICML/3. Bayesian Learning and Graphical Model/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/3. Bayesian Learning and Graphical Model/desktop.ini -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/A Deep and Tractable Density Estimator.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/A Deep and Tractable Density Estimator.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/An Empirical Exploration of Recurrent Network Architectures.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/An Empirical Exploration of Recurrent Network Architectures.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/BilBOWA Fast Bilingual Distributed Representations without Word Alignments.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/BilBOWA Fast Bilingual Distributed Representations without Word Alignments.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Compressing Neural Networks with the Hashing Trick.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Compressing Neural Networks with the Hashing Trick.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/DRAW A Recurrent Neural Network For Image Generation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/DRAW A Recurrent Neural Network For Image Generation.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/DeCAF.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/DeCAF.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Deep AutoRegressive Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Deep AutoRegressive Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Deep Edge-Aware Filters.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Deep Edge-Aware Filters.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Deep Generative Stochastic Networks Trainable by Backprop.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Deep Generative Stochastic Networks Trainable by Backprop.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Deep Learning with Limited Numerical Precision.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Deep Learning with Limited Numerical Precision.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Gated Feedback Recurrent Neural Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Gated Feedback Recurrent Neural Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Generative Moment Matching Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Generative Moment Matching Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Hierarchical Conditional Random Fields for Outlier Detection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Hierarchical Conditional Random Fields for Outlier Detection.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Latent Semantic Representation Learning for Scene Classification.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Latent Semantic Representation Learning for Scene Classification.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Learning Ordered Representations with Nested Dropout.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Learning Ordered Representations with Nested Dropout.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Learning Program Embeddings to Propagate Feedback on Student Code.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Learning Program Embeddings to Propagate Feedback on Student Code.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Learning Transferable Features with Deep Adaptation Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Learning Transferable Features with Deep Adaptation Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Learning by Stretching Deep Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Learning by Stretching Deep Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Learning to Disentangle Factors of Variation with Manifold Interaction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Learning to Disentangle Factors of Variation with Manifold Interaction.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/MADE Masked Autoencoder for Distribution Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/MADE Masked Autoencoder for Distribution Estimation.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Marginalized Denoising Auto-encoders for Nonlinear Representations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Marginalized Denoising Auto-encoders for Nonlinear Representations.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Modeling Order in Neural Word Embeddings at Scale.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Modeling Order in Neural Word Embeddings at Scale.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Neural Variational Inference and Learning in Belief Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Neural Variational Inference and Learning in Belief Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/On Deep Multi-View Representation Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/On Deep Multi-View Representation Learning.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Optimizing Neural Networks with Kronecker-factored Approximate Curvature.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Optimizing Neural Networks with Kronecker-factored Approximate Curvature.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Recurrent Convolutional Neural Networks for Scene Labeling.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Recurrent Convolutional Neural Networks for Scene Labeling.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Show Attend and Tell Neural Image Caption Generation with Visual Attention.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Show Attend and Tell Neural Image Caption Generation with Visual Attention.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Signal recovery from Pooling Representations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Signal recovery from Pooling Representations.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Stable and Efficient Representation Learning with Nonnegativity Constraints.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Stable and Efficient Representation Learning with Nonnegativity Constraints.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Stochastic Backpropagation and Approximate Inference in Deep Generative Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Stochastic Backpropagation and Approximate Inference in Deep Generative Models.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Structured Recurrent Temporal Restricted Boltzmann Machines.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Structured Recurrent Temporal Restricted Boltzmann Machines.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Unsupervised Domain Adaptation by Backpropagation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Unsupervised Domain Adaptation by Backpropagation.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Unsupervised Learning of Video Representations using LSTMs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Unsupervised Learning of Video Representations using LSTMs.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Variational Generative Stochastic Networks with Collaborative Shaping.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Variational Generative Stochastic Networks with Collaborative Shaping.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/Weight Uncertainty in Neural Network.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/Weight Uncertainty in Neural Network.txt -------------------------------------------------------------------------------- /data/ICML/4. Deep Learning/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/4. Deep Learning/desktop.ini -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/A Convergence Rate Analysis for LogitBoost, MART and Their Variant.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/A Convergence Rate Analysis for LogitBoost, MART and Their Variant.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Boosting with Online Binary Learners for the Multiclass Bandit Problem.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Boosting with Online Binary Learners for the Multiclass Bandit Problem.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Deep Boosting.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Deep Boosting.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Dynamic Programming Boosting for Discriminative Macro-Action Discovery.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Dynamic Programming Boosting for Discriminative Macro-Action Discovery.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Ensemble Methods for Structured Prediction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Ensemble Methods for Structured Prediction.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Ensemble-Based Tracking.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Ensemble-Based Tracking.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Gaussian Process Classification and Active Learning with Multiple Annotators.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Gaussian Process Classification and Active Learning with Multiple Annotators.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Latent Confusion Analysis by Normalized Gamma Construction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Latent Confusion Analysis by Normalized Gamma Construction.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Narrowing the Gap.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Narrowing the Gap.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/Near-Optimally Teaching the Crowd to Classify.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/Near-Optimally Teaching the Crowd to Classify.txt -------------------------------------------------------------------------------- /data/ICML/5. Ensemble and Crowdsourcing/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/5. Ensemble and Crowdsourcing/desktop.ini -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/A Unified Framework for Outlier-Robust PCA-like Algorithms.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/A Unified Framework for Outlier-Robust PCA-like Algorithms.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Circulant Binary Embedding.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Circulant Binary Embedding.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Coding for Random Projections.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Coding for Random Projections.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Compact Random Feature Maps.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Compact Random Feature Maps.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Composite Quantization for Approximate Nearest Neighbor Search.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Composite Quantization for Approximate Nearest Neighbor Search.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Discriminative Features via Generalized Eigenvectors.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Discriminative Features via Generalized Eigenvectors.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Efficient Learning of Mahalanobis Metrics for Ranking.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Efficient Learning of Mahalanobis Metrics for Ranking.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Factorized Point Process Intensities.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Factorized Point Process Intensities.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Large-Margin Metric Learning for Constrained Partitioning Problems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Large-Margin Metric Learning for Constrained Partitioning Problems.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Large-margin Weakly Supervised Dimensionality Reduction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Large-margin Weakly Supervised Dimensionality Reduction.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Low Rank Approximation using Error Correcting Coding Matrices.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Low Rank Approximation using Error Correcting Coding Matrices.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Making Fisher Discriminant Analysis Scalable.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Making Fisher Discriminant Analysis Scalable.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Memory and Computation Efficient PCA via Very Sparse Random Projections.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Memory and Computation Efficient PCA via Very Sparse Random Projections.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Multivariate Maximal Correlation Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Multivariate Maximal Correlation Analysis.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Multiview Triplet Embedding Learning Attributes in Multiple Maps.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Multiview Triplet Embedding Learning Attributes in Multiple Maps.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Nearest Neighbors Using Compact Sparse Codes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Nearest Neighbors Using Compact Sparse Codes.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Optimal Mean Robust Principal Component Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Optimal Mean Robust Principal Component Analysis.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Randomized Nonlinear Component Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Randomized Nonlinear Component Analysis.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Robust Inverse Covariance Estimation under Noisy Measurements.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Robust Inverse Covariance Estimation under Noisy Measurements.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Robust Principal Component Analysis with Complex Noise.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Robust Principal Component Analysis with Complex Noise.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Sparse meta-Gaussian information bottleneck.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Sparse meta-Gaussian information bottleneck.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Stochastic Neighbor Compression.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Stochastic Neighbor Compression.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/Streaming Sparse Principal Component Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/Streaming Sparse Principal Component Analysis.txt -------------------------------------------------------------------------------- /data/ICML/6. Feature Learning/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/6. Feature Learning/desktop.ini -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/A Divide-and-Conquer Solver for Kernel Support Vector Machines.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/A Divide-and-Conquer Solver for Kernel Support Vector Machines.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/A Kernel Independence Test for Random Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/A Kernel Independence Test for Random Processes.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/A Unifying View of Representer Theorems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/A Unifying View of Representer Theorems.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/A low variance consistent test of relative dependency.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/A low variance consistent test of relative dependency.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Controversy in mechanistic modelling with Gaussian processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Controversy in mechanistic modelling with Gaussian processes.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Distributed Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Distributed Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Double Nystrm Method An Efficient and Accurate Nystrm Scheme for Large-Scale Data Sets.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Double Nystrm Method An Efficient and Accurate Nystrm Scheme for Large-Scale Data Sets.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Finding Galaxies in the Shadows of Quasars with Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Finding Galaxies in the Shadows of Quasars with Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Kernel Interpolation for Scalable Structured Gaussian Processes KISS-GP.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Kernel Interpolation for Scalable Structured Gaussian Processes KISS-GP.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Kernel Mean Estimation and Stein Effect.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Kernel Mean Estimation and Stein Effect.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Maximum Mean Discrepancy for Class Ratio Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Maximum Mean Discrepancy for Class Ratio Estimation.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Memory Efficient Kernel Approximation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Memory Efficient Kernel Approximation.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Nonparametric Estimation of Multi-View Latent Variable Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Nonparametric Estimation of Multi-View Latent Variable Models.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Robust and Efficient Kernel Hyperparameter Paths with Guarantees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Robust and Efficient Kernel Hyperparameter Paths with Guarantees.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Sparse Variational Inference for Generalized GP Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Sparse Variational Inference for Generalized GP Models.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/The Kendall and Mallows Kernels for Permutations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/The Kendall and Mallows Kernels for Permutations.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/Variational Inference for Gaussian Process Modulated Poisson Processes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/Variational Inference for Gaussian Process Modulated Poisson Processes.txt -------------------------------------------------------------------------------- /data/ICML/7. Kernel Methods/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/7. Kernel Methods/desktop.ini -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/A Bayesian Framework for Online Classifier Ensemble.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/A Bayesian Framework for Online Classifier Ensemble.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/A Physics-Based Model Prior for Object-Oriented MDPs.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/A Physics-Based Model Prior for Object-Oriented MDPs.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Adaptive Belief Propagation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Adaptive Belief Propagation.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Adaptive Monte Carlo via Bandit Allocation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Adaptive Monte Carlo via Bandit Allocation.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Adaptivity and Optimism.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Adaptivity and Optimism.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/An Online Learning Algorithm for Bilinear Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/An Online Learning Algorithm for Bilinear Models.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Cascading Bandits Learning to Rank in the Cascade Model.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Cascading Bandits Learning to Rank in the Cascade Model.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Cheap Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Cheap Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Covering Number for Efficient Heuristic-based POMDP Planning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Covering Number for Efficient Heuristic-based POMDP Planning.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Efficient Learning in Large-Scale Combinatorial Semi-Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Efficient Learning in Large-Scale Combinatorial Semi-Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Following the Perturbed Leader for Online Structured Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Following the Perturbed Leader for Online Structured Learning.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Latent Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Latent Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Learning Complex Neural Network Policies with Trajectory Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Learning Complex Neural Network Policies with Trajectory Optimization.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Linear Programming for Large-Scale Markov Decision Problems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Linear Programming for Large-Scale Markov Decision Problems.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/On the convergence of no-regret learning in selfish routing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/On the convergence of no-regret learning in selfish routing.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/One Practical Algorithm for Both Stochastic and Adversarial Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/One Practical Algorithm for Both Stochastic and Adversarial Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Online Clustering of Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Online Clustering of Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Online Learning in Markov Decision Processes with Changing Cost Sequences.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Online Learning in Markov Decision Processes with Changing Cost Sequences.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Online Multi-Task Learning for Policy Gradient Methods.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Online Multi-Task Learning for Policy Gradient Methods.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Online Stochastic Optimization under Correlated Bandit Feedback.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Online Stochastic Optimization under Correlated Bandit Feedback.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Online Time Series Prediction with Missing Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Online Time Series Prediction with Missing Data.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Prediction with Limited Advice and Multiarmed Bandits with Paid Observations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Prediction with Limited Advice and Multiarmed Bandits with Paid Observations.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Preference-Based Rank Elicitation using Statistical Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Preference-Based Rank Elicitation using Statistical Models.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Pursuit-Evasion Without Regret, with an Application to Trading.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Pursuit-Evasion Without Regret, with an Application to Trading.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Qualitative Multi-Armed Bandits A Quantile-Based Approach.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Qualitative Multi-Armed Bandits A Quantile-Based Approach.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Reducing Dueling Bandits to Cardinal Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Reducing Dueling Bandits to Cardinal Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Simple regret for infinitely many armed bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Simple regret for infinitely many armed bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Sparse Reinforcement Learning via Convex Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Sparse Reinforcement Learning via Convex Optimization.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Spectral Bandits for Smooth Graph Functions.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Spectral Bandits for Smooth Graph Functions.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Statistical analysis of stochastic gradient methods for generalized linear models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Statistical analysis of stochastic gradient methods for generalized linear models.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Strongly Adaptive Online Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Strongly Adaptive Online Learning.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Taming the Monster.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Taming the Monster.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/The Hedge Algorithm on a Continuum.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/The Hedge Algorithm on a Continuum.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Thompson Sampling for Complex Online Problems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Thompson Sampling for Complex Online Problems.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Tracking Adversarial Targets.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Tracking Adversarial Targets.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/Unimodal Bandits.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/Unimodal Bandits.txt -------------------------------------------------------------------------------- /data/ICML/8. Online Learning/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/8. Online Learning/desktop.ini -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Deep Semi-NMF Model for Learning Hidden Representations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Deep Semi-NMF Model for Learning Hidden Representations.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A General Analysis of the Convergence of ADMM.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A General Analysis of the Convergence of ADMM.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Lower Bound for the Optimization of Finite Sums.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Lower Bound for the Optimization of Finite Sums.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Nearly-Linear Time Framework for Graph-Structured Sparsity.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Nearly-Linear Time Framework for Graph-Structured Sparsity.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Statistical Perspective on Algorithmic Leveraging.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Statistical Perspective on Algorithmic Leveraging.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/A Unified Framework for Consistency of Regularized Loss Minimizers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/A Unified Framework for Consistency of Regularized Loss Minimizers.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Adaptive Stochastic Alternating Direction Method of Multipliers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Adaptive Stochastic Alternating Direction Method of Multipliers.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Adding vs Averaging in Distributed Primal-Dual Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Adding vs Averaging in Distributed Primal-Dual Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Adding vs. Averaging in Distributed Primal-Dual Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Adding vs. Averaging in Distributed Primal-Dual Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/An Asynchronous Parallel Stochastic Coordinate Descent Algorithm.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/An Asynchronous Parallel Stochastic Coordinate Descent Algorithm.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Asynchronous Distributed ADMM for Consensus Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Asynchronous Distributed ADMM for Consensus Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Blitz A Principled Meta-Algorithm for Scaling Sparse Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Blitz A Principled Meta-Algorithm for Scaling Sparse Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Buffer k-d Trees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Buffer k-d Trees.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/CUR Algorithm for Partially Observed Matrices.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/CUR Algorithm for Partially Observed Matrices.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Cold-start Active Learning with Robust Ordinal Matrix Factorization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Cold-start Active Learning with Robust Ordinal Matrix Factorization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Communication-Efficient Distributed Optimization using an Approximate Newton-type Method.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Communication-Efficient Distributed Optimization using an Approximate Newton-type Method.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Complete Dictionary Recovery Using Nonconvex Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Complete Dictionary Recovery Using Nonconvex Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Convex Total Least Squares.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Convex Total Least Squares.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Coresets for Nonparametric Estimation the Case of DP-Means.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Coresets for Nonparametric Estimation the Case of DP-Means.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/DiSCO Distributed Optimization for Self-Concordant Empirical Loss.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/DiSCO Distributed Optimization for Self-Concordant Empirical Loss.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Dual Query.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Dual Query.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Efficient Algorithms for Robust One-bit Compressive Sensing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Efficient Algorithms for Robust One-bit Compressive Sensing.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Elementary Estimators for High-Dimensional Linear Regression.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Elementary Estimators for High-Dimensional Linear Regression.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Fast Multi-stage Submodular Maximization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Fast Multi-stage Submodular Maximization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Fast Stochastic Alternating Direction Method of Multipliers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Fast Stochastic Alternating Direction Method of Multipliers.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Faster cover trees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Faster cover trees.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Finito.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Finito.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Geometric Conditions for Subspace-Sparse Recovery.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Geometric Conditions for Subspace-Sparse Recovery.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Guaranteed Tensor Decomposition A Moment Approach.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Guaranteed Tensor Decomposition A Moment Approach.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Intersecting Faces Non-negative Matrix Factorization With New Guarantees.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Intersecting Faces Non-negative Matrix Factorization With New Guarantees.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/K-hyperplane Hinge-Minimax Classifier.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/K-hyperplane Hinge-Minimax Classifier.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Large-scale Multi-label Learning with Missing Labels.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Large-scale Multi-label Learning with Missing Labels.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Large-scale log-determinant computation through stochastic Chebyshev expansions.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Large-scale log-determinant computation through stochastic Chebyshev expansions.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Least Squares Revisited.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Least Squares Revisited.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Linear Time Solver for Primal SVM.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Linear Time Solver for Primal SVM.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Low-Rank Matrix Recovery from Row-and-Column Affine Measurements.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Low-Rank Matrix Recovery from Row-and-Column Affine Measurements.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Making the Most of Bag of Words.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Making the Most of Bag of Words.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Margins, Kernels and Non-linear Smoothed Perceptrons.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Margins, Kernels and Non-linear Smoothed Perceptrons.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Mind the duality gap safer rules for the Lasso.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Mind the duality gap safer rules for the Lasso.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Multi-label Classification via Feature-aware Implicit Label Space Encoding.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Multi-label Classification via Feature-aware Implicit Label Space Encoding.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Multiresolution Matrix Factorization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Multiresolution Matrix Factorization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Nonlinear Information-Theoretic Compressive Measurement Design.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Nonlinear Information-Theoretic Compressive Measurement Design.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Nuclear Norm Minimization via Active Subspace Selection.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Nuclear Norm Minimization via Active Subspace Selection.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/On Greedy Maximization of Entropy.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/On Greedy Maximization of Entropy.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Online Learning of Eigenvectors.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Online Learning of Eigenvectors.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Optimal Budget Allocation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Optimal Budget Allocation.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Outlier Path.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Outlier Path.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/PASSCoDe Parallel ASynchronous Stochastic dual Co-ordinate Descent.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/PASSCoDe Parallel ASynchronous Stochastic dual Co-ordinate Descent.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/PU Learning for Matrix Completion.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/PU Learning for Matrix Completion.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Probabilistic Matrix Factorization with Non-random Missing Data.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Probabilistic Matrix Factorization with Non-random Missing Data.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Rank-One Matrix Pursuit for Matrix Completion.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Rank-One Matrix Pursuit for Matrix Completion.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Riemannian Pursuit for Big Matrix Recovery.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Riemannian Pursuit for Big Matrix Recovery.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Saddle Points and Accelerated Perceptron Algorithms.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Saddle Points and Accelerated Perceptron Algorithms.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Safe Screening with Variational Inequalities and Its Application to Lasso.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Safe Screening with Variational Inequalities and Its Application to Lasso.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Sample-based approximate regularization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Sample-based approximate regularization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Scaling SVM and Least Absolute Deviations via Exact Data Reduction.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Scaling SVM and Least Absolute Deviations via Exact Data Reduction.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Sparse Subspace Clustering with Missing Entries.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Sparse Subspace Clustering with Missing Entries.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Statistical-Computational Phase Transitions in Planted Models.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Statistical-Computational Phase Transitions in Planted Models.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Stay on path PCA along graph paths.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Stay on path PCA along graph paths.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Stochastic Dual Coordinate Ascent with Adaptive Probabilities.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Stochastic Dual Coordinate Ascent with Adaptive Probabilities.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Stochastic Optimization with Importance Sampling for Regularized Loss Minimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Stochastic Optimization with Importance Sampling for Regularized Loss Minimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Structured Low-Rank Matrix Factorization.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Structured Low-Rank Matrix Factorization.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Submodularity in Data Subset Selection and Active Learning.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Submodularity in Data Subset Selection and Active Learning.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Swept Approximate Message Passing for Sparse Estimation.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Swept Approximate Message Passing for Sparse Estimation.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/The Power of Randomization Distributed Submodular Maximization on Massive Datasets.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/The Power of Randomization Distributed Submodular Maximization on Massive Datasets.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/Towards an optimal stochastic alternating direction method of multipliers.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/Towards an optimal stochastic alternating direction method of multipliers.txt -------------------------------------------------------------------------------- /data/ICML/9. Optimization/desktop.ini: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/ICML/9. Optimization/desktop.ini -------------------------------------------------------------------------------- /data/SGD/covtype-testing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/SGD/covtype-testing.txt -------------------------------------------------------------------------------- /data/SGD/covtype-training.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/SGD/covtype-training.txt -------------------------------------------------------------------------------- /data/SGD/dataset1-a9a-testing.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/SGD/dataset1-a9a-testing.txt -------------------------------------------------------------------------------- /data/SGD/dataset1-a9a-training.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/SGD/dataset1-a9a-training.txt -------------------------------------------------------------------------------- /data/StopWords/english: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/data/StopWords/english -------------------------------------------------------------------------------- /projectutil.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/projectutil.py -------------------------------------------------------------------------------- /runAssignment1.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/runAssignment1.py -------------------------------------------------------------------------------- /runAssignment2.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/runAssignment2.py -------------------------------------------------------------------------------- /runAssignment3.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/runAssignment3.py -------------------------------------------------------------------------------- /runAssignment4.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/runAssignment4.py -------------------------------------------------------------------------------- /runAssignment5.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/runAssignment5.py -------------------------------------------------------------------------------- /tests/EnglishTextDataProcessingTest.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/tests/EnglishTextDataProcessingTest.py -------------------------------------------------------------------------------- /tests/ReductionTest.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/tests/ReductionTest.py -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keyunluo/data-mining-course/HEAD/tests/__init__.py --------------------------------------------------------------------------------