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- W2954056989 abstract "We study the stochastic multi-armed bandit problem in the case when the arm samples are dependent over time and generated from so-called weak $cC$-mixing processes. We establish a $cC-$Mix Improved UCB agorithm and provide both problem-dependent and independent regret analysis in two different scenarios. In the first, so-called fast-mixing scenario, we show that pseudo-regret enjoys the same upper bound (up to a factor) as for i.i.d. observations; whereas in the second, slow mixing scenario, we discover a surprising effect, that the regret upper bound is similar to the independent case, with an incremental {em additive} term which does not depend on the number of arms. The analysis of slow mixing scenario is supported with a minmax lower bound, which (up to a $log(T)$ factor) matches the obtained upper bound." @default.
- W2954056989 created "2019-07-12" @default.
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- W2954056989 date "2020-12-18" @default.
- W2954056989 modified "2023-09-26" @default.
- W2954056989 title "Restless dependent bandits with fading memory" @default.
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