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