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• W2947200456 abstract "The information-theoretic analysis by Russo and Van Roy (2014) in combination with minimax duality has proved a powerful tool for the analysis of online learning algorithms in full and partial information settings. In most applications there is a tantalising similarity to the classical analysis based on mirror descent. We make a formal connection, showing that the information-theoretic bounds in most applications can be derived from existing techniques for online convex optimisation. Besides this, for $k$-armed adversarial bandits we provide an efficient algorithm with regret that matches the best information-theoretic upper bound and improve best known regret guarantees for online linear optimisation on $ell_p$-balls and bandits with graph feedback." @default.
• W2947200456 created "2019-06-07" @default.
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• W2947200456 date "2019-05-28" @default.
• W2947200456 modified "2023-09-27" @default.
• W2947200456 title "Connections Between Mirror Descent, Thompson Sampling and the Information Ratio" @default.
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