├── README.md ├── create_markdown.py └── scraper.py /README.md: -------------------------------------------------------------------------------- 1 | # ICML 2015 Papers 2 | 3 | ### ICML released an utterly useless unlinked list of papers accepted for the 2015 conference. 4 | ### So I hacked up a scraper and got links to all the papers available on arxiv. 5 | ### This list includes ~60% of all papers accepted. 6 | 7 | #### Please do advise with any papers I may have missed due to title quirks, etc. I will likely update this to publicly available papers that are not on arxiv. 8 | 9 | 10 | 1. [Approval Voting and Incentives in Crowdsourcing](http://arxiv.org/abs/1502.05696) 11 | 2. [[1406.3852] A low variance consistent test of relative ... - arXiv](http://arxiv.org/abs/1406.3852) 12 | 3. [Spectral Clustering via the Power Method -- Provably - arXiv](http://arxiv.org/abs/1311.2854) 13 | 4. [[1312.4564] Adaptive Stochastic Alternating Direction ... - arXiv](http://arxiv.org/abs/1312.4564) 14 | 5. [A Lower Bound for the Optimization of Finite Sums](http://arxiv.org/abs/1410.0723) 15 | 6. [Learning Word Representations with Hierarchical Sparse ...](http://arxiv.org/abs/1406.2035) 16 | 7. [Learning Transferable Features with Deep Adaptation ...](http://arxiv.org/abs/1502.02791) 17 | 8. [How transferable are features in deep neural networks?](http://arxiv.org/abs/1411.1792) 18 | 9. [On the Relationship between Sum-Product Networks ... - arXiv](http://arxiv.org/abs/1501.01239) 19 | 10. [[1505.00526] An Explicit Sampling Dependent Spectral ...](http://arxiv.org/abs/1505.00526) 20 | 11. [A Stochastic PCA and SVD Algorithm with an Exponential ...](http://arxiv.org/abs/1409.2848) 21 | 12. [Learning Local Invariant Mahalanobis Distances](http://arxiv.org/abs/1502.01176) 22 | 13. [[1501.03273] Classification with Low Rank and Missing Data](http://arxiv.org/abs/1501.03273) 23 | 14. [Telling cause from effect in deterministic linear dynamical ...](http://arxiv.org/abs/1503.01299) 24 | 15. [High Dimensional Bayesian Optimisation and Bandits via ...](http://arxiv.org/abs/1503.01673) 25 | 16. [[1504.03991] Theory of Dual-sparse Regularized ... - arXiv](http://arxiv.org/abs/1504.03991) 26 | 17. [A General Analysis of the Convergence of ADMM](http://arxiv.org/abs/1502.02009) 27 | 18. [Stochastic Primal-Dual Coordinate Method for Regularized ...](http://arxiv.org/abs/1409.3257) 28 | 19. [Spectral MLE: Top-$ K $ Rank Aggregation from Pairwise ...](http://arxiv.org/abs/1504.07218) 29 | 20. [Exploring Algorithmic Limits of Matrix Rank Minimization ...](http://arxiv.org/abs/1406.2504) 30 | 21. [Batch Normalization: Accelerating Deep Network Training ...](http://arxiv.org/abs/1502.03167) 31 | 22. [Distributed Estimation of Generalized Matrix Rank: Efficient ...](http://arxiv.org/abs/1502.01403) 32 | 23. [[1402.5876] Manifold Gaussian Processes for Regression](http://arxiv.org/abs/1402.5876) 33 | 24. [Online Regret Bounds for Undiscounted Continuous ... - arXiv](http://arxiv.org/abs/1302.2550) 34 | 25. [The Fundamental Incompatibility of Hamiltonian Monte ...](http://arxiv.org/abs/1502.01510) 35 | 26. [Faster Rates for the Frank-Wolfe Method over Strongly ...](http://arxiv.org/abs/1406.1305) 36 | 27. [Online Tracking by Learning Discriminative Saliency Map ...](http://arxiv.org/abs/1502.06796) 37 | 28. [A Statistical Perspective on Randomized Sketching for ...](http://arxiv.org/abs/1406.5986) 38 | 29. [[1411.3224] On TD(0) with function approximation ... - arXiv](http://arxiv.org/abs/1411.3224) 39 | 30. [Learning Parametric-Output HMMs with Two Aliased States](http://arxiv.org/abs/1502.02158) 40 | 31. [Latent Gaussian Processes for Distribution Estimation of ...](http://arxiv.org/abs/1503.02182) 41 | 32. [Variational inference for sparse spectrum Gaussian process ...](http://arxiv.org/abs/1306.1999) 42 | 33. [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](http://arxiv.org/abs/1502.08053) 43 | 34. [JUMP-Means: Small-Variance Asymptotics for Markov Jump ...](http://arxiv.org/abs/1503.00332) 44 | 35. [[1211.0358] Deep Gaussian Processes - arXiv](http://arxiv.org/abs/1211.0358) 45 | 36. [Fast Bilingual Distributed Representations without Word ...](http://arxiv.org/abs/1410.2455) 46 | 37. [Cascading Bandits](http://arxiv.org/abs/1502.02763) 47 | 38. [Random Coordinate Descent Methods for Minimizing ...](http://arxiv.org/abs/1502.02643) 48 | 39. [Counterfactual Risk Minimization: Learning from Logged ...](http://arxiv.org/abs/1502.02362) 49 | 40. [A Linear Dynamical System Model for Text](http://arxiv.org/abs/1502.04081) 50 | 41. [Unsupervised Learning of Video Representations using ...](http://arxiv.org/abs/1502.04681) 51 | 42. [MADE: Masked Autoencoder for Distribution Estimation](http://arxiv.org/abs/1502.03509) 52 | 43. [Large-scale Log-determinant Computation through ...](http://arxiv.org/abs/1503.06394) 53 | 44. [Differentially Private Bayesian Optimization](http://arxiv.org/abs/1501.04080) 54 | 45. [Rademacher Observations, Private Data, and Boosting](http://arxiv.org/abs/1502.02322) 55 | 46. [Bayesian and empirical Bayesian forests](http://arxiv.org/abs/1502.02312) 56 | 47. [The Ladder: A Reliable Leaderboard for Machine Learning ...](http://arxiv.org/abs/1502.04585) 57 | 48. [Enabling scalable stochastic gradient-based inference for ...](http://arxiv.org/abs/1501.05427) 58 | 49. [Reified Context Models](http://arxiv.org/abs/1502.06665) 59 | 50. [Learning Fast-Mixing Models for Structured Prediction](http://arxiv.org/abs/1502.06668) 60 | 51. [[1406.6947] Deep Learning Multi-View Representation for ...](http://arxiv.org/abs/1406.6947) 61 | 52. [[1406.7443] Efficient Learning in Large-Scale Combinatorial ...](http://arxiv.org/abs/1406.7443) 62 | 53. [[1406.4311] Sparse Estimation with the Swept ... - arXiv](http://arxiv.org/abs/1406.4311) 63 | 54. [Unsupervised Domain Adaptation by Backpropagation](http://arxiv.org/abs/1409.7495) 64 | 55. [Markov Chain Monte Carlo and Variational Inference ...](http://arxiv.org/abs/1410.6460) 65 | 56. [The Power of Randomization: Distributed Submodular ...](http://arxiv.org/abs/1502.02606) 66 | 57. [Non-Gaussian Discriminative Factor Models via the Max ...](http://arxiv.org/abs/1504.07468) 67 | 58. [Nested Sequential Monte Carlo Methods](http://arxiv.org/abs/1502.02536) 68 | 59. [[1402.1389] Distributed Variational Inference in Sparse ...](http://arxiv.org/abs/1402.1389) 69 | 60. [[1402.1412] Variational Inference in Sparse Gaussian ...](http://arxiv.org/abs/1402.1412) 70 | 61. [Rebuilding Factorized Information Criterion: Asymptotically ...](http://arxiv.org/abs/1504.05665) 71 | 62. [[1311.0776] The Composition Theorem for Differential Privacy](http://arxiv.org/abs/1311.0776) 72 | 63. [Strongly Adaptive Online Learning](http://arxiv.org/abs/1502.07073) 73 | 64. [[1411.0860] CUR Algorithm for Partially Observed Matrices](http://arxiv.org/abs/1411.0860) 74 | 65. [Scaling-up Empirical Risk Minimization: Optimization of ...](http://arxiv.org/abs/1501.02629) 75 | 66. [Towards a Learning Theory of Causation](http://arxiv.org/abs/1502.02398) 76 | 67. [DRAW: A Recurrent Neural Network For Image Generation](http://arxiv.org/abs/1502.04623) 77 | 68. [Distributed Gaussian Processes](http://arxiv.org/abs/1502.02843) 78 | 69. [[1302.2684] A Tensor Approach to Learning Mixed ... - arXiv](http://arxiv.org/abs/1302.2684) 79 | 70. [Consistent Estimation of Dynamic and Multi-layer Networks](http://arxiv.org/abs/1410.8597) 80 | 71. [[1405.3229] Rate of Convergence and Error Bounds for ...](http://arxiv.org/abs/1405.3229) 81 | 72. [Convex Learning of Multiple Tasks and their Structure - arXiv](http://arxiv.org/abs/1504.03101) 82 | 73. [[1304.5610] Tight Performance Bounds for Approximate ...](http://arxiv.org/abs/1304.5610) 83 | 74. [Approximate Modified Policy Iteration](http://arxiv.org/abs/1205.3054) 84 | 75. [Long Short-Term Memory Over Tree Structures](http://arxiv.org/abs/1503.04881) 85 | 76. [Predictive Entropy Search for Bayesian Optimization with ...](http://arxiv.org/abs/1502.05312) 86 | 77. [Generative Moment Matching Networks](http://arxiv.org/abs/1502.02761) 87 | 78. [Deep Learning with Limited Numerical Precision](http://arxiv.org/abs/1502.02551) 88 | 79. [Teaching Deep Convolutional Neural Networks to Play Go](http://arxiv.org/abs/1412.3409) 89 | 80. [Kernel Interpolation for Scalable Structured Gaussian ...](http://arxiv.org/abs/1503.01057) 90 | 81. [[1407.2538] Learning Deep Structured Models - arXiv](http://arxiv.org/abs/1407.2538) 91 | 82. [Personalized PageRank Solution Paths](http://arxiv.org/abs/1503.00322) 92 | 83. [Scalable Variational Inference in Log-supermodular Models](http://arxiv.org/abs/1502.06531) 93 | 84. [Variational Inference for Gaussian Process Modulated ...](http://arxiv.org/abs/1411.0254) 94 | 85. [Probabilistic Backpropagation for Scalable Learning of ...](http://arxiv.org/abs/1502.05336) 95 | 86. [Trust Region Policy Optimization](http://arxiv.org/abs/1502.05477) 96 | 87. [[1410.5518] On Symmetric and Asymmetric LSHs for Inner ...](http://arxiv.org/abs/1410.5518) 97 | 88. [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508) 98 | 89. [Feature-Budgeted Random Forest](http://arxiv.org/abs/1502.05925) 99 | 90. [Show, Attend and Tell: Neural Image Caption Generation ...](http://arxiv.org/abs/1502.03044) 100 | 91. [Learning to Search Better Than Your Teacher](http://arxiv.org/abs/1502.02206) 101 | 92. [Gated Feedback Recurrent Neural Networks](http://arxiv.org/abs/1502.02367) 102 | 93. [[1502.03671] Phrase-based Image Captioning - arXiv](http://arxiv.org/abs/1502.03671) 103 | 94. [Gradient-based Hyperparameter Optimization through ...](http://arxiv.org/abs/1502.03492) 104 | 95. [[1406.1901] Subsampling Methods for Persistent Homology](http://arxiv.org/abs/1406.1901) 105 | 96. [Binary Embedding: Fundamental Limits and Fast Algorithm](http://arxiv.org/abs/1502.05746) 106 | 97. [Scalable Bayesian Optimization Using Deep Neural Networks](http://arxiv.org/abs/1502.05700) 107 | 98. [Scalable Nonparametric Bayesian Inference on Point ...](http://arxiv.org/abs/1410.6834) 108 | 99. [Deep Unsupervised Learning using Nonequilibrium ...](http://arxiv.org/abs/1503.03585) 109 | 100. [Compressing Neural Networks with the Hashing Trick - arXiv](http://arxiv.org/abs/1504.04788) 110 | 101. [Optimal and Adaptive Algorithms for Online Boosting](http://arxiv.org/abs/1502.02651) 111 | 102. [[1411.1134] Global Convergence of Stochastic Gradient ...](http://arxiv.org/abs/1411.1134) 112 | 103. [[1504.06785] Complete Dictionary Recovery over the Sphere](http://arxiv.org/abs/1504.06785) 113 | 104. [PASSCoDe: Parallel ASynchronous Stochastic dual Co ...](http://arxiv.org/abs/1504.01365) 114 | 105. [Optimizing Neural Networks with Kronecker-factored ...](http://arxiv.org/abs/1503.05671) 115 | 106. [Novelty Detection Under Multi-Instance Multi-Label ... - arXiv](http://arxiv.org/abs/1311.6211) 116 | 107. [[1212.4663] Concentration of Measure Inequalities in ...](http://arxiv.org/abs/1212.4663) 117 | 108. [PU Learning for Matrix Completion](http://arxiv.org/abs/1411.6081) 118 | 109. [A Distributed Proximal Method for Composite Convex ...](http://arxiv.org/abs/1409.8547) 119 | 110. [Posterior Sampling and Stochastic Gradient Monte Carlo](http://arxiv.org/abs/1502.07645) 120 | 111. [Inference for Partially Observed Multitype Branching ...](http://arxiv.org/abs/0902.4520) 121 | -------------------------------------------------------------------------------- /create_markdown.py: -------------------------------------------------------------------------------- 1 | import cPickle as pickle 2 | import operator 3 | def readResults(): 4 | l = pickle.load(open("arxivLinksTitles.pkl", 'rb')) 5 | arxivs = reduce(operator.add, filter(lambda x: len(x) > 0, l)) 6 | return arxivs 7 | 8 | def toLinks(arxivs): 9 | return map(lambda x: '[' + x[0] +']' + '(' + x[1] + ')', arxivs) 10 | 11 | def toNumberedList(links): 12 | return '\n'.join(map(lambda x,y: str(1 + x) + '.' + ' ' + y, range(len(links)), links)) 13 | 14 | def write_markdown(file_name, links): 15 | f=open(file_name, 'wb') 16 | f.write(links) 17 | f.close() 18 | 19 | def wrapper(): 20 | arxs = readResults() 21 | lxs = toLinks(arxs) 22 | nums = toNumberedList(lxs) 23 | write_markdown('icml2015_arxiv.md', nums) 24 | 25 | if __name__ == '__main__': 26 | wrapper() 27 | 28 | -------------------------------------------------------------------------------- /scraper.py: -------------------------------------------------------------------------------- 1 | import cPickle as pickle 2 | from urllib2 import urlopen 3 | from bs4 import BeautifulSoup 4 | import time 5 | from multiprocessing.dummy import Pool 6 | from sh import curl 7 | 8 | def get_search_terms(): 9 | html = urlopen("http://icml.cc/2015/?page_id=710") 10 | soup = BeautifulSoup(html) 11 | papers = soup.findAll("tr", "ro2") 12 | titles = map(lambda x: x.find("td").text, papers) 13 | terms = map(lambda x: x.replace(' ', '+').encode('utf-8').strip(), titles) 14 | return terms 15 | 16 | def search_single(term): 17 | UA = "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36" 18 | base = "https://www.google.com/search?q=" 19 | query = base + term 20 | res = curl(query, A=UA) 21 | soup = BeautifulSoup(res.stdout) 22 | return soup 23 | 24 | def GrabLinkWithAnchor(single_res): 25 | return [single_res.text, single_res['href']] 26 | 27 | def parse_single(soup_res): 28 | links = map(lambda x: x.find("a"), soup_res.findAll("div", "rc")) 29 | AnchorsLinks = map(lambda x: GrabLinkWithAnchor(x), links) 30 | arxivs = filter(lambda x: x[1].startswith("http://arxiv.org/abs"), AnchorsLinks) 31 | return arxivs 32 | 33 | def single_wrapper(term): 34 | sp = search_single(term) 35 | print "grabbed %s now sleeping 2 seconds" % term 36 | time.sleep(2) 37 | return parse_single(sp) 38 | 39 | def parallel_wrapper(term_list): 40 | p = Pool(3) 41 | res = p.map(single_wrapper, term_list) 42 | p.close() 43 | p.join() 44 | return res 45 | 46 | if __name__ == '__main__': 47 | terms = get_search_terms() 48 | arxiv_results = parallel_wrapper(terms) 49 | pickle.dump(arxiv_results, open('arxivLinksTitles.pkl', 'wb')) 50 | --------------------------------------------------------------------------------