Web: http://arxiv.org/abs/2209.07330

Sept. 16, 2022, 1:12 a.m. | Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa

cs.LG updates on arXiv.org arxiv.org

We study best-arm identification with a fixed budget and contextual
(covariate) information in stochastic multi-armed bandit problems. In each
round, after observing contextual information, we choose a treatment arm using
past observations and current context. Our goal is to identify the best
treatment arm, a treatment arm with the maximal expected reward marginalized
over the contextual distribution, with a minimal probability of
misidentification. First, we derive semiparametric lower bounds for this
problem, where we regard the gaps between the expected …

arm arxiv identification information

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