├── AAAI-2022-AutoGraph ├── Part1-GNN.pdf ├── Part2-HPO.pdf └── Part3-KGR.pdf ├── ACML-2021-AutoGSD ├── ACML-AutoGra-qm.pdf ├── ACML-AutoKGE-yq.pdf └── AutoGraph@ACML.pdf ├── IJCAI-2021-AutoRecsys ├── README.md └── slides │ ├── Part2_Adv.pdf │ ├── part1-quanming.pdf │ ├── part3-AutoGNN.pdf │ └── part4-AutoKGE.pdf ├── KDD-2020-AutoRecsys ├── README.md ├── images │ ├── 4PA.jpg │ ├── KDD.png │ └── THU.jpg └── slides │ ├── .gitignore │ ├── Part1-AutoML.pdf │ ├── Part2-RS.pdf │ ├── Part3_Adv.pdf │ ├── Part4-AutoGraph.pdf │ └── Part5-KG.pdf ├── README.md └── Valse-2021-Talk └── 20211010-Valse.pdf /AAAI-2022-AutoGraph/Part1-GNN.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/AAAI-2022-AutoGraph/Part1-GNN.pdf -------------------------------------------------------------------------------- /AAAI-2022-AutoGraph/Part2-HPO.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/AAAI-2022-AutoGraph/Part2-HPO.pdf -------------------------------------------------------------------------------- /AAAI-2022-AutoGraph/Part3-KGR.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/AAAI-2022-AutoGraph/Part3-KGR.pdf -------------------------------------------------------------------------------- /ACML-2021-AutoGSD/ACML-AutoGra-qm.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/ACML-2021-AutoGSD/ACML-AutoGra-qm.pdf -------------------------------------------------------------------------------- /ACML-2021-AutoGSD/ACML-AutoKGE-yq.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/ACML-2021-AutoGSD/ACML-AutoKGE-yq.pdf -------------------------------------------------------------------------------- /ACML-2021-AutoGSD/AutoGraph@ACML.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/ACML-2021-AutoGSD/AutoGraph@ACML.pdf -------------------------------------------------------------------------------- /IJCAI-2021-AutoRecsys/README.md: -------------------------------------------------------------------------------- 1 | This tutorial is part of **IJCAI-2021 Tutorial: Advances in Recommender Systems** [(official site)](https://quanmingyao.github.io/AutoML.github.io/ijcai21-tutorial.html). 2 | 3 | #### Part 1: An introduction to Automated Machine Learning (AutoML) 4 | 5 | > Speaker: [Dr. Quanming Yao](http://www.cse.ust.hk/~qyaoaa/) (Tsinghua/4Paradigm) 6 | 7 | > Slides: [LINK](slides/part1-quanming.pdf) 8 | 9 | #### Part 2: Why AutoML is Needed in RecSys and Recent Advances 10 | 11 | > Speaker: [Mr. Chen Gao](https://scholar.google.com/citations?user=Af60_cEAAAAJ&hl=en) (Tsinghua) 12 | 13 | > Slides: [LINK](slides/Part2_Adv.pdf) 14 | 15 | #### Part 3: Automated Graph Neural Network for RecSys 16 | 17 | > Speaker: [Dr. Huan Zhao](https://hzhaoaf.github.io/) (4Paradigm) 18 | 19 | > Slides: [LINK](slides/part3-AutoGNN.pdf) 20 | 21 | #### Part 4: Automated Knowledge Graph Embedding 22 | 23 | > Speaker: [Dr. Yongqi Zhang](https://scholar.google.com/citations?user=nVk-7EAAAAAJ&hl=zh-CN) (4Paradigm) 24 | 25 | > Slides: [LINK](slides/part4-AutoKGE.pdf) 26 | 27 | #### Related Publications 28 | 29 | If you feel the tutorial is helpful, please consider ack to related papers in the follow 30 | 31 | 1. F. Hutter, L. Kotthoff, and J. Vanschoren, Automated machine learning: methods, systems, challenges, Springer Nature, 2019. 32 | 33 | 2. Q. Yao and M. Wang, Taking human out of learning applications: A survey on automated machine learning, tech. rep., arXiv preprint, 2018. 34 | 3. P. Resnick and H. R. Varian, Recommender systems, CACM, 1997. 35 | 4. T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, in ICLR, 2016. 36 | 5. H. Zhao, Q. Yao, J. Li, Y. Song, and D. Lee, Meta-graph based recommendation fusion over heterogeneous information networks, in SIGKDD, 2017. 37 | 6. Q. Yao, X. Chen, J. T. Kwok, Y. Li, and C.-J. Hsieh, Efficient neural interaction function search for collaborative filtering, in WebConf, 2020. 38 | 7. Y. Luo, M. Wang, H. Zhou, Q. Yao, W.-W. Tu, Y. Chen, W. Dai, and Q. Yang, AutoCross: Automatic feature crossing for tabular data in real-world applications, in SIGKDD, 2019. 39 | 8. Y. Zheng, C. Gao, L. Chen, D. Jin, and Y. Li, DGCN: Diversified recommendation with graph convolutional networks, WebConf, 2021. 40 | 9. B. Jin, C. Gao, X. He, D. Jin, and Y. Li, Multi-behavior recommendation with graph convolutional networks, in SIGIR, 2020. 41 | 10. S. Liu, C. Gao, Y. Chen, D. Jin, and Y. Li, Learnable embedding sizes for recommender systems, in ICLR, 2021. 42 | 11. R. Ying, R. He, K. Chen,P. Eksombatchai, W. Hamilton, and J. Leskovec, Graph convolutional neural networks for web-scale recommender systems, in KDD, 2018. 43 | 12. X. Wang, X. He, M. Wang, F. Feng, and T.S. Chua. 2019. Neural graph collaborative filtering, in SIGIR, 2019. 44 | 13. Y. Gao, H. Yang, P. Zhang, C. Zhou, and Y. Hu, GraphNAS: Graph neural architecture search with reinforcement learning, in IJCAI, 2020. 45 | 14. J. You, R. Ying, and J. Leskovec, Design Space for Graph Neural Networks, in NeurIPS, 2020. 46 | 15. H. Zhao, L. Wei, and Q. Yao, Simplifying Architecture Search for Graph Neural Network, in CIKM-CSSA, 2020. 47 | 16. H. Zhao, Q. Yao, and W. Tu, Search to aggregate neighborhood for graph neural network, in ICDE, 2021. 48 | 17. Z. Zhang, X. Wang, and W. Zhu, Automated Machine Learning on Graphs: A Survey, in IJCAI, 2021. 49 | 18. S. Ji, S. Pan, E. Cambria, P. Marttinen and P. S. Yu, A Survey on Knowledge Graphs: Representation, Acquisition and Applications, in TNNLS 2021. 50 | 19. Z. Sun, Z. Deng, J. Nie and J. Tang, RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space, in ICLR 2019. 51 | 20. X. Wang, X. He, Y. Cao, M. Liu and T. Chua, KGAT: Knowledge Graph Attention Network for Recommendation, in KDD 2019. 52 | 21. Y. Zhang, Q. Yao, W. Dai and L. Chen, AutoSF: Searching Scoring Functions for Knowledge Graph Embedding, in ICDE 2020. 53 | 22. Y. Zhang, Q. Yao and L. Chen, Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding, in NeurIPS 2020. 54 | 55 | -------------------------------------------------------------------------------- /IJCAI-2021-AutoRecsys/slides/Part2_Adv.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/IJCAI-2021-AutoRecsys/slides/Part2_Adv.pdf -------------------------------------------------------------------------------- /IJCAI-2021-AutoRecsys/slides/part1-quanming.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/IJCAI-2021-AutoRecsys/slides/part1-quanming.pdf -------------------------------------------------------------------------------- /IJCAI-2021-AutoRecsys/slides/part3-AutoGNN.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/IJCAI-2021-AutoRecsys/slides/part3-AutoGNN.pdf -------------------------------------------------------------------------------- /IJCAI-2021-AutoRecsys/slides/part4-AutoKGE.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/IJCAI-2021-AutoRecsys/slides/part4-AutoKGE.pdf -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/README.md: -------------------------------------------------------------------------------- 1 | # KDD-2020-Tutorial: Automated Recommender System 2 | 3 | [![KDD-2020](images/KDD.png "KDD-2020")](images/KDD.png) [![KDD-2020](images/4PA.jpg "4Paradigm")](images/4PA.jpg) [![KDD-2020](images/THU.jpg "Tsinghua")](images/THU.jpg) 4 | 5 | As the recommendation tasks are getting more diverse and the recommending models are growing more complicated, it is increasingly challenging to develop a proper recommendation system that can adapt well to a new recommendation task. In this tutorial, we focus on how automated machine learning (AutoML) techniques can benefit the design and usage of recommender systems. Specifically, we start from a full scope describing what can be automated for recommendation systems. Then, we elaborate more on three important topics under such a scope, i.e., feature engineering, hyperparameter optimization/neural architecture search, and algorithm selection. The core issues and recent works under these topics will be introduced, summarized, and discussed. Finally, we finalize the tutorial with conclusions and some future directions. 6 | 7 | > Dates: 2020/08/24 - 4.00-8.00AM (Beijing Time) 8 | 9 | The Tutorial is hold online, and is avaliable via [Zoom](https://zoom.us/download) at: 10 | 11 | > Zoom ID: https://zoom.us/j/99655780785, Password: RnNhVXhhYmZRL0E5Y04rOUM5Uk9ndz09 12 | 13 | This tutorial is part of "KDD 2020 Tutorial: Advances in Recommender Systems" [(official site)](https://sites.google.com/view/kdd20-marketplace-autorecsys/). 14 | 15 | ### What is Automated machine learning (AutoML) - A retrospective view 16 | > Speaker: [Dr. Quanming Yao](http://www.cse.ust.hk/~qyaoaa/) (4Paradigm) 17 | 18 | > Time: 4.00-5.00AM (50mins talk + 10mins QA) 19 | 20 | > Slides: [LINK](slides/Part1-AutoML.pdf) 21 | 22 | ### Recommender System: Basic and Why AutoML is Needed? 23 | > Speaker: [Prof. Yong Li](http://fi.ee.tsinghua.edu.cn/~liyong/) (Tsinghua) 24 | 25 | > Time: 5.00-5.40AM (35mins talk + 5mins QA) 26 | 27 | > Slides: [LINK](slides/Part2-RS.pdf) 28 | 29 | ### Recent Advances in Automated Recommender System 30 | > Speaker: [Mr. Chen Gao](https://scholar.google.com/citations?user=Af60_cEAAAAJ&hl=en) (Tsinghua) 31 | 32 | > Time: 5.40-6.20AM (35mins talk + 5mins QA) 33 | 34 | > Slides: [LINK](slides/Part3_Adv.pdf) 35 | 36 | ### Automated Graph Neural Network for Recommender System 37 | > Speaker: [Dr. Huan Zhao](https://hzhaoaf.github.io/) (4Paradigm) 38 | 39 | > Time: 6.20-7.00AM (35mins talk + 5mins QA) 40 | 41 | > Slides: [LINK](slides/Part4-AutoGraph.pdf) 42 | 43 | ### Automated Knowledge Graph Embedding 44 | > Speaker: [Dr. Yongqi Zhang](https://scholar.google.com/citations?user=nVk-7EAAAAAJ&hl=zh-CN) (4Paradigm) 45 | 46 | > Time: 7.00-7.40AM (35mins talk + 5mins QA) 47 | 48 | > Slides: [LINK](slides/Part5-KG.pdf) 49 | 50 | #### Related Publications 51 | - Y. Zhang, Q. Yao Neural Recurrent Structure Search for Knowledge Graph Embedding. International Workshop on Knowledge Graph@KDD. 2020. 52 | - Q. Yao, X. Chen, J. Kwok, Y. Li, C.-J. Hsieh. Efficient Neural Interaction Functions Search for Collaborative Filtering. Webconf. 2020 53 | - Q. Yao, J. Xu, W. Tu, Z. Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI. 2020 54 | - Y. Zhang, Q. Yao, W. Dai, L. Chen. AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. ICDE. 2020. 55 | - X. Wang, X. He, M. Wang, F. Feng, TS Chua. Neural graph collaborative filtering. SIGIR. 2019. 56 | - Y. Luo, M. Wang, H. Zhou, Q. Yao, W. Tu, Y. Chen, Q. Yang, W. Dai. AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications. KDD. 2019. 57 | - Y. Chen, B. Chen, X. He, C. Gao, Y. Li, J.-G. Lou, Y. Wang. LambdaOpt: Learn to Regularize Recommender Models in Finer Levels. KDD. 2019. 58 | - R. Ying, R. He, K. Chen, P. Eksombatchai, W. Hamilton, J. Leskovec. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD. 2019. 59 | - Q. Yao, M. Wang, Y. Li, W. Tu, Q. Yang, Y. Yu. Taking Human out of Learning Applications: A Survey on Automated Machine Learning. Arvix. Nov. 2018. 60 | - H. Zhao, Q. Yao, J. Li, Y. Song, D. Lee. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. KDD. 2017 61 | 62 | #### Citation 63 | 64 | If you feel the tutorial is helpful, please ack 65 | 66 | ``` 67 | @inproceedings{10.1145/3394486.3406463, 68 | author = {Mehrotra, Rishabh and Carterette, Ben and Li, Yong and Yao, Quanming and Gao, Chen and Kwok, James and Yang, Qiang and Guyon, Isabelle}, 69 | title = {Advances in Recommender Systems: From Multi-Stakeholder Marketplaces to Automated RecSys}, 70 | year = {2020}, 71 | url = {https://doi.org/10.1145/3394486.3406463}, 72 | series = {KDD '20} 73 | } 74 | ``` 75 | 76 | ## New Opportunities 77 | - Interns, research assistants, and researcher positions are available. See [requirement](http://www.cse.ust.hk/~qyaoaa/pages/job-ad.pdf) 78 | 79 | 80 | -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/images/4PA.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/images/4PA.jpg -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/images/KDD.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/images/KDD.png -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/images/THU.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/images/THU.jpg -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/slides/.gitignore: -------------------------------------------------------------------------------- 1 | *.pptx 2 | *.docx 3 | -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/slides/Part1-AutoML.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/slides/Part1-AutoML.pdf -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/slides/Part2-RS.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/slides/Part2-RS.pdf -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/slides/Part3_Adv.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/slides/Part3_Adv.pdf -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/slides/Part4-AutoGraph.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/slides/Part4-AutoGraph.pdf -------------------------------------------------------------------------------- /KDD-2020-AutoRecsys/slides/Part5-KG.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/KDD-2020-AutoRecsys/slides/Part5-KG.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This is a collection of slides we used on AutoML tutorials/workshops. 2 | 3 | - [AAAI 2022 Tutorial: Automated Learning from Graph-Structured Data](https://quanmingyao.github.io/AutoML.github.io/aaai22-tutorial.html) (2022.2) 4 | - [ACML 2021 Tutorial: Automated Learning from Graph-Structured Data](https://quanmingyao.github.io/AutoML.github.io/acml21-tutorial.html) (2021.11) 5 | - [VALSE 2021 Workshop Invited Talk: Automated representation learning from knowledge graphs](https://github.com/AutoML-Research/AutoML-Tutorial/blob/master/Valse-2021-Talk/20211010-Valse.pdf) (2021.10) 6 | - [IJCAI 2021 Tutorial: Towards automated recommender system](https://quanmingyao.github.io/AutoML.github.io/ijcai21-tutorial.html) (2021.8) 7 | - [IJCAI 2021 Tutorial: Automated Learning from Noisy Supervision](https://wsl-workshop.github.io/ijcai2021tutorial/part4.pdf) (2021.8) 8 | - [KDD 2020 Tutorial: Automated recommender system](https://sites.google.com/view/kdd20-marketplace-autorecsys/) (2020.8) 9 | -------------------------------------------------------------------------------- /Valse-2021-Talk/20211010-Valse.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LARS-research/AutoML-Tutorial/290f43c60ffffe9776cb775c8cae9077cebfc999/Valse-2021-Talk/20211010-Valse.pdf --------------------------------------------------------------------------------