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Information theory is deeply connected to two key tasks in machine learning: prediction and representation learning. Because of these connections, information theory has found wide applications in machine learning tasks, such as proving generalization bounds, certifying fairness and privacy, optimizing information content of unsupervised/supervised representations, and proving limitations to prediction performance. Conversely, progress in machine learning have been successfully applied to classical information theory tasks such as compression and transmission.
These recent progress have lead to new open questions and opportunities: to marry the simplicity and elegance of information theoretic analysis with the complexity of modern high dimensional machine learning setups. However, because of the diversity of information theoretic research, different communities often progress independently despite shared questions and tools. For example, variational bounds to mutual information are concurrently developed in information theory, generative model, and learning theory communities.
This workshop hopes to bring together researchers from different disciplines, identify common grounds, and spur discussion on how information theory can apply to and benefit from modern machine learning setups.
Fri 9:30 a.m. - 10:00 a.m.
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Invited Talk: Ayfer Ozgur Aydin
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Talk
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Fri 10:00 a.m. - 10:40 a.m.
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Invited Talk: Stefano Soatto and Alessandro Achille
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Talk
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Stefano Soatto · Alessandro Achille 🔗 |
Fri 11:00 a.m. - 11:45 a.m.
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Contributed Talk
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Talk
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Fri 2:00 p.m. - 2:30 p.m.
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Invited Talk: Varun Jog
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Talk
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Varun Jog 🔗 |
Fri 2:30 p.m. - 3:00 p.m.
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Invited Talk: Po-Ling Loh
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Talk
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Fri 3:20 p.m. - 3:50 p.m.
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Invited Talk: Aaron van den Oord
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Talk
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Fri 3:50 p.m. - 4:20 p.m.
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Invited Talk: Alexander A Alemi
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Talk
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Alexander Alemi 🔗 |
Fri 4:20 p.m. - 5:00 p.m.
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Poster Spotlight
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Spotlight
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Fri 5:00 p.m. - 6:00 p.m.
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Poster Session
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Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis
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Author Information
Shengjia Zhao (Stanford University)
Jiaming Song (Stanford University)
I am a first year Ph.D. student in Stanford University. I think about problems in machine learning and deep learning under the supervision of Stefano Ermon. I did my undergrad at Tsinghua University, where I was lucky enough to collaborate with Jun Zhu and Lawrence Carin on scalable Bayesian machine learning.
Yanjun Han (Stanford University)
Kristy Choi (Stanford University)
Pratyusha Kalluri (Stanford University)
Ben Poole (Google Brain)
Alex Dimakis (University of Texas, Austin)
Jiantao Jiao (University of California, Berkeley)
Tsachy Weissman (Stanford University)
Stefano Ermon (Stanford)
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Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
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Jiantao Jiao · Weihao Gao · Yanjun Han -
2018 Poster: Multi-Agent Generative Adversarial Imitation Learning »
Jiaming Song · Hongyu Ren · Dorsa Sadigh · Stefano Ermon -
2018 Poster: Entropy Rate Estimation for Markov Chains with Large State Space »
Yanjun Han · Jiantao Jiao · Chuan-Zheng Lee · Tsachy Weissman · Yihong Wu · Tiancheng Yu -
2018 Spotlight: Entropy Rate Estimation for Markov Chains with Large State Space »
Yanjun Han · Jiantao Jiao · Chuan-Zheng Lee · Tsachy Weissman · Yihong Wu · Tiancheng Yu -
2018 Spotlight: The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal »
Jiantao Jiao · Weihao Gao · Yanjun Han -
2018 Poster: Constructing Unrestricted Adversarial Examples with Generative Models »
Yang Song · Rui Shu · Nate Kushman · Stefano Ermon -
2018 Poster: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Spotlight: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Poster: Amortized Inference Regularization »
Rui Shu · Hung Bui · Shengjia Zhao · Mykel J Kochenderfer · Stefano Ermon -
2017 : Generative Adversarial Imitation Learning, Stefano Ermon, Stanford »
Stefano Ermon -
2017 Workshop: NIPS Highlights (MLTrain), Learn How to code a paper with state of the art frameworks »
Alex Dimakis · Nikolaos Vasiloglou · Guy Van den Broeck · Alexander Ihler · Assaf Araki -
2017 : Stefano Ermon (Stanford): Measuring Progress Towards Sustainable Development Goals with Machine Learning »
Stefano Ermon -
2017 Poster: Streaming Weak Submodularity: Interpreting Neural Networks on the Fly »
Ethan Elenberg · Alex Dimakis · Moran Feldman · Amin Karbasi -
2017 Poster: A-NICE-MC: Adversarial Training for MCMC »
Jiaming Song · Shengjia Zhao · Stefano Ermon -
2017 Oral: Streaming Weak Submodularity: Interpreting Neural Networks on the Fly »
Ethan Elenberg · Alex Dimakis · Moran Feldman · Amin Karbasi -
2017 Poster: Model-Powered Conditional Independence Test »
Rajat Sen · Ananda Theertha Suresh · Karthikeyan Shanmugam · Alex Dimakis · Sanjay Shakkottai -
2017 Poster: InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations »
Yunzhu Li · Jiaming Song · Stefano Ermon -
2017 Poster: Neural Variational Inference and Learning in Undirected Graphical Models »
Volodymyr Kuleshov · Stefano Ermon -
2016 Poster: Leveraging Sparsity for Efficient Submodular Data Summarization »
Erik Lindgren · Shanshan Wu · Alex Dimakis -
2016 Poster: Single Pass PCA of Matrix Products »
Shanshan Wu · Srinadh Bhojanapalli · Sujay Sanghavi · Alex Dimakis -
2016 Poster: Solving Marginal MAP Problems with NP Oracles and Parity Constraints »
Yexiang Xue · zhiyuan li · Stefano Ermon · Carla Gomes · Bart Selman -
2016 Poster: Generative Adversarial Imitation Learning »
Jonathan Ho · Stefano Ermon -
2016 Poster: Variational Bayes on Monte Carlo Steroids »
Aditya Grover · Stefano Ermon -
2016 Poster: Adaptive Concentration Inequalities for Sequential Decision Problems »
Shengjia Zhao · Enze Zhou · Ashish Sabharwal · Stefano Ermon -
2015 Poster: Orthogonal NMF through Subspace Exploration »
Megasthenis Asteris · Dimitris Papailiopoulos · Alex Dimakis -
2015 Poster: Sparse PCA via Bipartite Matchings »
Megasthenis Asteris · Dimitris Papailiopoulos · Anastasios Kyrillidis · Alex Dimakis -
2015 Poster: Learning Causal Graphs with Small Interventions »
Karthikeyan Shanmugam · Murat Kocaoglu · Alex Dimakis · Sriram Vishwanath -
2014 Poster: Sparse Polynomial Learning and Graph Sketching »
Murat Kocaoglu · Karthikeyan Shanmugam · Alex Dimakis · Adam Klivans -
2014 Poster: On the Information Theoretic Limits of Learning Ising Models »
Rashish Tandon · Karthikeyan Shanmugam · Pradeep Ravikumar · Alex Dimakis -
2014 Oral: Sparse Polynomial Learning and Graph Sketching »
Murat Kocaoglu · Karthikeyan Shanmugam · Alex Dimakis · Adam Klivans -
2013 Poster: Embed and Project: Discrete Sampling with Universal Hashing »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2012 Poster: Density Propagation and Improved Bounds on the Partition Function »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Poster: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Spotlight: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman