Reinforcement Learning Day 2019

Venue: 11 Times Square
Room 6501
New York, NY 10036

Contact: For event questions, please contact [email protected]

We shared the latest research on learning to make decisions based on feedback at Reinforcement Learning Day 2019

Reinforcement learning is the study of decision making with consequences over time. The topic draws together multi-disciplinary efforts from computer science, cognitive science, mathematics, economics, control theory, and neuroscience. The common thread through all of these studies is: how do natural and artificial systems learn to make decisions in complex environments based on external, and possibly delayed, feedback.

This workshop featured talks by a number of outstanding speakers whose research covers a broad swath of the topic, from statistics to neuroscience, from computer science to control. A key objective is to bring together the research communities of all these areas to learn from each other and build on the latest knowledge.

Committee Chairs

Hal Daumé III, Microsoft Research
Akshay Krishnamurthy, Microsoft Research

Speakers

Asli Celikyilmaz, MSR Redmond
Christopher Amato, Northeastern University
Finale Doshi-Velez, Harvard University
Geoff Gordon, MSR Montréal
Mengdi Wang, Princeton University
Philip Thomas, University of Massachusetts, Amherst
Sam Devlin, MSR Cambridge
Sheila McIlraith, University of Toronto

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