Feb. 13, 2024, 5:44 a.m. | Junpei Komiyama Shinji Ito Yuichi Yoshida Souta Koshino

cs.LG updates on arXiv.org arxiv.org

This work is motivated by the growing demand for reproducible machine learning. We study the stochastic multi-armed bandit problem. In particular, we consider a replicable algorithm that ensures, with high probability, that the algorithm's sequence of actions is not affected by the randomness inherent in the dataset. We observe that existing algorithms require $O(1/\rho^2)$ times more regret than nonreplicable algorithms, where $\rho$ is the level of nonreplication. However, we demonstrate that this additional cost is unnecessary when the time horizon …

algorithm algorithms cs.lg dataset demand free machine machine learning multi-armed bandits observe probability randomness stat.ml stochastic study the algorithm work

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