Prediction with limited advice and multiarmed bandits with paid observations
Seldin, Yevgeny, Bartlett, Peter L., Crammer, Koby, & Abbasi-Yadkori, Yasin (2014) Prediction with limited advice and multiarmed bandits with paid observations. In International Conference on Machine Learning, 21–June 26, 2014, Beijing, China.
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)TlnN ) 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((cNlnN) 1/3 T2/3+√TlnN ) 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.
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|Item Type:||Conference Paper|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Copyright Owner:||Copyright 2014 [please consult the author]|
|Deposited On:||01 May 2014 01:24|
|Last Modified:||17 Jul 2014 07:28|
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