Real-world Sequential Decision Making: Reinforcement Learning and Beyond
Hoang Le · Yisong Yue · Adith Swaminathan · Byron Boots · Ching-An Cheng

Fri Jun 14th 02:00 -- 06:00 PM @ Seaside Ballroom

Workshop website:

This workshop aims to bring together researchers from industry and academia in order to describe recent advances and discuss future research directions pertaining to real-world sequential decision making, broadly construed. We aim to highlight new and emerging research opportunities for the machine learning community that arise from the evolving needs for making decision making theoretically and practically relevant for realistic applications.

Research interest in reinforcement and imitation learning has surged significantly over the past several years, with the empirical successes of self-playing in games and availability of increasingly realistic simulation environments. We believe the time is ripe for the research community to push beyond simulated domains and start exploring research directions that directly address the real-world need for optimal decision making. We are particularly interested in understanding the current theoretical and practical challenges that prevent broader adoption of current policy learning and evaluation algorithms in high-impact applications, across a broad range of domains.

This workshop welcomes both theory and application contributions.

02:00 PM Emma Brunskill (Stanford) - Minimizing & Understanding the Data Needed to Learn to Make Good Sequences of Decisions (Invited Talk) Emma Brunskill
02:30 PM Miro Dudík (Microsoft Research) - Doubly Robust Off-policy Evaluation with Shrinkage (Invited Talk) Miroslav Dudik
03:00 PM Poster Session Part 1 and Coffee Break (Poster Session)
04:00 PM Suchi Saria (John Hopkins) - Link between Causal Inference and Reinforcement Learning and Applications to Learning from Offline/Observational Data (Invited Talk) Suchi Saria
04:30 PM Dawn Woodard (Uber) - Dynamic Pricing and Matching for Ride-Hailing (Invited Talk) Dawn Woodard
05:00 PM Panel Discussion with Emma Brunskill, Miro Dudík, Suchi Saria, Dawn Woodard (Discussion Panel)
05:30 PM Poster Session Part 2 (Poster Session)

Author Information

Hoang Le (Caltech)

Hoang M. Le is a PhD Candidate in the Computing and Mathematical Sciences Department at the California Institute of Technology. He received a M.S. in Cognitive Systems and Interactive Media from the Universitat Pompeu Fabra, Barcelona, Spain, and a B.A. in Mathematics from Bucknell University in Lewisburg, PA. He is a recipient of an Amazon AI Fellowship. Hoang’s research focuses on the theory and applications of sequential decision making, with a strong focus on imitation learning. He has broad familiarity with the latest advances in imitation learning techniques and applications. His own research in imitation learning blends principled new techniques with a diverse range of application domains. In addition to popular reinforcement learning domains such as maze navigation and Atari games, his prior work on imitation learning has been applied to learning human behavior in team sports and developing automatic camera broadcasting system.

Yisong Yue (Caltech)

Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for interactive machine learning and structured prediction. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive planning & allocation problems.

Adith Swaminathan (Microsoft Research)
Byron Boots (Georgia Tech)
Ching-An Cheng (Georgia Tech)

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