Multi-Armed Bandits in Metric Spaces

Speaker: Alex Slivkins, Microsoft Research - SVC
Host: Prof. David KempeAbstract:
In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions.In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a complete solution for the multi-armed problem in this setting. That is, for every metric space (L,X) we define an isometry invariant which bounds from below the performance of Lipschitz MAB algorithms for X, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions. (STOC'08: joint work with Bobby Kleinberg and Eli Upfal)Biography:
Alex Slivkins is a researcher at Microsoft Research, Silicon Valley Center. Before that he has been an undergrad at Caltech, a grad student at Cornell CS dept, and a postdoc at Brown. He is interested in the design and analysis of algorithms, the theory of large distributed networks, and machine learning. Specific topics of interest include metric embeddings, locality-aware overlay networks, and multi-armed bandits.

Location: James H. Zumberge Hall Of Science (ZHS) - 163

Audiences: Everyone Is Invited

Contact: CS Colloquia