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arXiv · 1403.5341

An Information-Theoretic Analysis of Thompson Sampling

Daniel Russo·Benjamin Van Roy
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Abstract

We provide an information-theoretic analysis of Thompson sampling that applies across a broad range of online optimization problems in which a decision-maker must learn from partial feedback. This analysis inherits the simplicity and elegance of information theory and leads to regret bounds that scale with the entropy of the optimal-action distribution. This strengthens preexisting results and yields new insight into how information improves performance.

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Daniel Russo, Benjamin Van Roy. 2014-03-21. An Information-Theoretic Analysis of Thompson Sampling. https://arxiv.org/abs/1403.5341

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