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- W2098258765 abstract "We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of K actions in response to the observed context, and observes the reward only for that action. Our method assumes access to an oracle for solving fully supervised cost-sensitive classification problems and achieves the statistically optimal regret guarantee with only O(√KT) oracle calls across all T rounds. By doing so, we obtain the most practical contextual bandit learning algorithm amongst approaches that work for general policy classes. We conduct a proof-of-concept experiment which demonstrates the excellent computational and statistical performance of (an online variant of) our algorithm relative to several strong baselines." @default.
- W2098258765 created "2016-06-24" @default.
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- W2098258765 date "2014-06-21" @default.
- W2098258765 modified "2023-09-29" @default.
- W2098258765 title "Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits" @default.
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