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Activity Number: 219
Type: Invited
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Marketing
Abstract - #307287
Title: Thompson Sampling for Solving Multi-Armed Bandits
Author(s): Lihong Li*+ and Olivier Chapelle
Companies: Microsoft Research and Criteo
Keywords: Thompson sampling ; probability matching ; multi-armed bandit
Abstract:

Thompson sampling is one of oldest heuristic to address the exploration/exploitation trade-off, the defining challenge in multi-armed bandits, but it is surprisingly unpopular in the literature. We first present some empirical results using Thompson sampling on simulated and real data, and show that it is highly competitive. Since this heuristic is very easy to implement, we argue that it should be part of the standard baselines to compare against. Second, we review recent theoretical advances that analyze finite-time performance of Thompson sampling in the framework of regret minimizing, and conclude with a few open problems.


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