English(EN) Fundamental Limitations of Fixed-Budget Best-Arm Identification

研究揭示最优臂识别算法的基本局限性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-13 14:50

一篇新发表在arXiv上的研究论文探讨了固定预算最优臂识别算法的基本局限性。研究表明，对于该领域的任何算法，都至少存在一个问题实例，其误差衰减率显著低于静态预知型算法（预先知道臂均值）。这一发现回答了2022年提出的一个开放性问题，表明固定预算最优臂识别不承认复杂度类别。 AI

影响这项研究强调了不确定性下决策算法的理论约束，可能影响未来自适应系统的设计。

排序理由该集群包含一篇详细介绍机器学习问题理论局限性的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Motti Goldberger · 2026-07-14 04:00

固定预算最优臂识别的基本局限性

arXiv:2607.11635v1 Announce Type: cross Abstract: In fixed-budget best-arm identification, also known as ranking and selection, an algorithm has a sampling budget to distribute across $K$ arms. Each sample provides noisy feedback about that arm's mean, and the goal is to identify…
arXiv stat.ML TIER_1 English(EN) · Motti Goldberger · 2026-07-13 14:50

固定预算最优臂识别的基本局限性

In fixed-budget best-arm identification, also known as ranking and selection, an algorithm has a sampling budget to distribute across $K$ arms. Each sample provides noisy feedback about that arm's mean, and the goal is to identify the arm with the largest mean. A common performan…