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- W3136378527 abstract "Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the dependence of regret on prior uncertainty. However, this dependence is through entropy, which can become arbitrarily large as the number of actions increases. We establish new bounds that depend instead on a notion of rate-distortion. Among other things, this allows us to recover through information-theoretic arguments a near-optimal bound for the linear bandit. We also offer a bound for the logistic bandit that dramatically improves on the best previously available, though this bound depends on an information-theoretic statistic that we have only been able to quantify via computation." @default.
- W3136378527 created "2021-03-29" @default.
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- W3136378527 date "2018-05-30" @default.
- W3136378527 modified "2023-10-08" @default.
- W3136378527 title "An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces" @default.
- W3136378527 hasPublicationYear "2018" @default.
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