Events Calendar

MASCLE Machine Learning Seminar: Robert Schapire (Microsoft Research NYC) - The Contextual Bandits Problem: Techniques for Learning to Make High-Reward Decisions

Speaker: Robert Schapire, Microsoft Research NYC

Talk Title: The Contextual Bandits Problem: Techniques for Learning to Make High-Reward Decisions

Series: NVIDIA Distinguished Lecture Series in Machine Learning

Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.

We consider how to learn through experience to make intelligent decisions. In the generic setting, called the contextual bandits problem, the learner must repeatedly decide which action to take in response to an observed context, and is then permitted to observe the received reward, but only for the chosen action. The goal is to learn to behave nearly as well as the best policy (or decision rule) in some possibly very large and rich space of candidate policies. This talk will describe progress on developing general methods for this problem and some of its variants.


Biography: Robert Schapire is a Principal Researcher at Microsoft Research in New York City. He received his PhD from MIT in 1991. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991. In 2002, he became a Professor of Computer Science at Princeton University. He joined Microsoft Research in 2014. His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). He is a fellow of the AAAI, and a member of both the National Academy of Engineering and the National Academy of Sciences. His main research interest is in theoretical and applied machine learning, with particular focus on boosting, online learning, game theory, and maximum entropy.


Host: Haipeng Luo

Location: Henry Salvatori Computer Science Center (SAL) - 101

Audiences: Everyone Is Invited

Contact: Computer Science Department