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• W2911363708 abstract "We prove a new minimax theorem connecting the worst-case Bayesian regret and minimax regret under partial monitoring with no assumptions on the space of signals or decisions of the adversary. We then generalise the information-theoretic tools of Russo and Van Roy (2016) for proving Bayesian regret bounds and combine them with the minimax theorem to derive minimax regret bounds for various partial monitoring settings. The highlight is a clean analysis of `non-degenerate easy' and `hard' finite partial monitoring, with new regret bounds that are independent of arbitrarily large game-dependent constants. The power of the generalised machinery is further demonstrated by proving that the minimax regret for k-armed adversarial bandits is at most sqrt{2kn}, improving on existing results by a factor of 2. Finally, we provide a simple analysis of the cops and robbers game, also improving best known constants." @default.
• W2911363708 created "2019-02-21" @default.
• W2911363708 creator A5017031238 @default.
• W2911363708 creator A5069856068 @default.
• W2911363708 date "2019-02-01" @default.
• W2911363708 modified "2023-10-05" @default.
• W2911363708 title "An Information-Theoretic Approach to Minimax Regret in Partial Monitoring" @default.
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• W2911363708 hasPublicationYear "2019" @default.
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