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Information Theory and Machine Learning
Shengjia Zhao · Jiaming Song · Yanjun Han · Kristy Choi · Pratyusha Kalluri · Ben Poole · Alexandros Dimakis · Jiantao Jiao · Tsachy Weissman · Stefano Ermon

Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ East Exhibition Hall A
Event URL: https://sites.google.com/view/itml19 »

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.
Invited Talk: Ayfer Ozgur Aydin (Talk)
Fri 10:00 a.m. - 10:40 a.m.
Invited Talk: Stefano Soatto and Alessandro Achille (Talk)
Stefano Soatto, Alessandro Achille
Fri 11:00 a.m. - 11:45 a.m.
Contributed Talk (Talk)
Fri 2:00 p.m. - 2:30 p.m.
Invited Talk: Varun Jog (Talk)
Varun Jog
Fri 2:30 p.m. - 3:00 p.m.
Invited Talk: Po-Ling Loh (Talk)
Fri 3:20 p.m. - 3:50 p.m.
Invited Talk: Aaron van den Oord (Talk)
Fri 3:50 p.m. - 4:20 p.m.
Invited Talk: Alexander A Alemi (Talk)
Alex Alemi
Fri 4:20 p.m. - 5:00 p.m.
Poster Spotlight (Spotlight)
Fri 5:00 p.m. - 6:00 p.m.
Poster Session
Greg 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, Alex Alemi, Bao-Ngoc Nguyen, Rob Brekelmans, Tailin Wu, Aditya Mahajan, Alex Li, Kiran 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, Nick 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

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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