English(EN) Characterizing Bias in Post-Bandit Inference under Index Algorithms

新研究表征了后强盗推断算法中的偏差

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-04 04:00

一篇新发表在arXiv上的论文分析了当强盗算法用于为下游推断生成数据时引入的偏差。该研究侧重于稳定的指数算法，包括上限置信度1（UCB1）及其变体，并提供了样本均值偏差和期望Z统计量的详细表达式。研究揭示了算法遗憾与偏差之间的权衡，表明更具探索性的算法会以增加遗憾为代价来减少偏差。 AI

影响为理解和潜在地减轻强盗算法生成数据中的偏差提供了一个理论框架，强盗算法是强化学习和自适应系统的基础。

排序理由发表在arXiv上的学术论文，详细介绍了对算法偏差的新分析。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Lisu Wang, Yilun Chen, Jiaqi Lu · 2026-08-04 04:00

在索引算法下的后强盗推断中表征偏差

arXiv:2608.01069v1 Announce Type: cross Abstract: Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable index algorithms, including UCB1 and its generalizations, and derive sharp leading-or…