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- W1972090598 abstract "State of the art algorithms for stochastic multi-armed bandit problem are based on either one of the two principles: optimism in face of uncertainty, and probability matching. In this paper, we provide a unified approach to combine these principles. The major result is a new upper confidence bound formula, UCB-max, by which the p-value of an arm's UCB value roughly matches the arm's probability of been optimal. Our numerical study comparing UCB-max with other competitors (UCB-tuned, UCB-V, MOSS, KL-UCB and Thompson sampling) shows that UCB-max is remarkably efficient and stable." @default.
- W1972090598 created "2016-06-24" @default.
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- W1972090598 date "2014-12-01" @default.
- W1972090598 modified "2023-09-26" @default.
- W1972090598 title "Maximal expectation as upper confidence bound for multi-armed bandit problems" @default.
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- W1972090598 doi "https://doi.org/10.1109/itaic.2014.7065060" @default.
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