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|Abstract:||In this paper we study the contextual bandit problem (also known as the multi-armed bandit problem with expert advice) for linear Payoff functions. For T rounds, K actions, and d dimensional feature vectors, we prove an O(√Td ln 3(KT ln(T)/δ)) regret bound that holds with probability 1-δ for the simplest known (both conceptually and computationally) efficient upper confidence bound algorithm for this problem. We also prove a lower bound of Ω( √Td) for this setting, matching the upper bound up to logarithmic factors. Copyright 2011 by the authors.|
|Citation:||Chu, W, Li, L, Reyzin, L, Schapire, RE. (2011). Contextual bandits with linear Payoff functions. Journal of Machine Learning Research, 15 (208 - 214|
|Pages:||208 - 214|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Journal of Machine Learning Research|
|Version:||Final published version. This is an open access article.|
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