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An Information-Theoretic Analysis of Thompson Sampling

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.

preprint2015arXivOpen access
Daniel RussoBenjamin Van Roy
Machine Learning
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Open access2 authors1 topic
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Daniel RussoBenjamin Van Roy

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