English(EN) Bayesian Fixed-Confidence Best-Arm Identification with Abstention

用于具有弃权选项的最优臂识别的新型贝叶斯算法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种名为Screen-and-Confirm (S&C) 的新算法，用于固定置信度贝叶斯最优臂识别问题。该方法允许学习者在满足目标错误概率和弃权预算的前提下，选择不做出最终推荐。文章分析了该算法的预期停止时间，并推导了最小预期停止时间。对于高斯先验和奖励，研究建立了匹配的上下界，并指出几乎并列的情况会显著影响采样成本。 AI

影响引入了一种新的算法方法，用于在具有弃权能力的机器学习环境中优化决策。

排序理由该集群包含一篇详细介绍机器学习新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 4 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍机器学习新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yuqi Huang, Yunlong Hou, Vincent Y. F. Tan · 2026-10-23 04:00

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…

本文如何被排名

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Bayesian Fixed-Confidence Best-Arm Identification with Abstention

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