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- W1498977598 abstract "We study two problems of online learning under restricted information access. In the first problem, prediction with limited advice, we consider a game of prediction with expert advice, where on each round of the game we query the advice of a subset of M out of N experts. We present an algorithm that achieves O(√N/M T ln N) regret on T rounds of this game. The second problem, the multiarmed bandit with paid observations, is a variant of the adversarial N-armed bandit game, where on round t of the game we can observe the reward of any number of arms, but each observation has a cost c. We present an algorithm that achieves O((cN lnN)1/3 T2/3 + √T lnN) regret on T rounds of this game in the worst case. Furthermore, we present a number of refinements that treat arm- and time-dependent observation costs and achieve lower regret under benign conditions. We present lower bounds that show that, apart from the logarithmic factors, the worst-case regret bounds cannot be improved." @default.
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- W1498977598 date "2014-06-21" @default.
- W1498977598 modified "2023-09-23" @default.
- W1498977598 title "Prediction with Limited Advice and Multiarmed Bandits with Paid Observations" @default.
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