Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence

Abstract: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, we propose \emph{Halve-Then-Greedy} (HTG), which combines a large initial balanced allocation, successive halving, and posterior-greedy sampling. We prove instance-dependent, finite-budget exponential bounds on posterior-failure probability that capture both separation from unfavorable arms and the presence of multiple favorable arms. On one-positive-arm families, we establish an asymptotic sample-complexity lower bound that HTG matches up to a logarithmic factor. We further analyze uniform sampling and sequential halving, revealing regimes where they require more samples than HTG by linear and logarithmic factors, respectively. Simulations illustrate HTG's sample-efficiency advantages and their dependence on instance structure.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2610.20997 [cs.LG]

(or arXiv:2610.20997v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.20997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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