Abstract:Fixed-budget best-arm identification (BAI) is a bandit problem where the learning agent maximizes the probability of identifying the optimal arm after a fixed number of observations. In this work, we initiate the study of this problem in the Bayesian setting. We propose a Bayesian elimination algorithm and derive an upper bound on the probability that it fails to identify the optimal arm. The bound reflects the quality of the prior and is the first such bound in this setting. We prove it using a frequentist-li… Show more
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