Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence
We study fixed-budget adaptive experimentation aimed at maximizing the probability of obtaining sufficient posterior evidence that a recommended design's mean performance exceeds a target. Unlike best-arm identification, this objective does not require finding the best design. For Gaussian bandits with a Gaussian prior...
Yi-Nan Li, Kwang-Sung Jun, Kapilan Balagopalan et al.
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