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New Bayesian algorithm for best-arm identification with abstention

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called Screen-and-Confirm (S&C) for Bayesian fixed-confidence best-arm identification problems. This method allows a learner to abstain from making a final recommendation, subject to a target error probability and an abstention budget. The algorithm's expected stopping time has been analyzed, and minimum expected stopping times have been derived. For Gaussian priors and rewards, the study establishes matching upper and lower bounds, highlighting that nearly tied instances significantly influence the sampling cost. AI

IMPACT Introduces a new algorithmic approach for optimizing decision-making in machine learning contexts with abstention capabilities.

RANK_REASON The cluster contains a research paper detailing a novel algorithm for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Bayesian algorithm for best-arm identification with abstention

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The cluster contains a research paper detailing a novel algorithm for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuqi Huang, Yunlong Hou, Vincent Y. F. Tan ·

    Bayesian Fixed-Confidence Best-Arm Identification with Abstention

    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 propos…

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