SPIN Unprocessed October 23, 2026 ai_technology research
Bayesian Fixed-Confidence Best-Arm Identification with Abstention
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arXiv:2610.30384v1 Announce Type: new Abstract: We study the Bayesian fixed-confidence best-arm identification problem in which a learner can abstain from making a terminal recommendation. Subject to a target error probability $\delta$ and an abstention budget $\alpha$, we propose Screen-and-Confirm (S&C) and analyze its expected stopping time. We further derive the minimum expected stopping time for any algorithm that satisfies these constraints. For Gaussian priors and rewards, as $(\delta,\al
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