Rob Schapire

"Playing repeated games: Theory, an algorithm, applications"

Abstract:

This talk will describe a simple, general algorithm for learning to play any matrix game against an unknown adversary.  The algorithm, which is based directly on Littlestone and Warmuth's weighted majority algorithm, can be shown never to perform much worse than the best fixed strategy, even if selected in hindsight.  Moreover, because of the algorithm's moderate resource requirements, it can be used even when working with extremely large game matrices.  Taken together, these properties make the algorithm a good fit for a range of machine-learning applications, including on-line learning and boosting.  Recently, the algorithm has also been applied to reinforcement learning, specifically, to the problem of learning to imitate the behavior of an "expert" while attempting simultaneously to improve on the expert's performance.

Bio:

Robert Schapire received his ScB in math and computer science from Brown University in 1986, and his SM (1988) and PhD (1991) from MIT under the supervision of Ronald Rivest. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991 where he remained for eleven years. At the end of 2002, he became a Professor of Computer Science at Princeton University. 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). His main research interest is in theoretical and applied machine learning.