研究人员开发了一种名为PROBE（Proxy OLS for Best-arm Exploration）的新算法，用于改进在奖励观察成本高昂时的决策制定。该算法利用来自机器学习和大型语言模型的廉价、相关的代理分数以及实际奖励数据。PROBE能有效地学习代理与奖励之间的相关性，与传统方法相比，尤其是在相关性较强的情况下，能显著节省样本。数值实验证明了其在汽车贷款定价等场景中的有效性。 AI
影响 该算法可能导致在利用AI预测的决策制定过程中更有效地利用数据。
排序理由 该集群包含一篇详细介绍统计问题新算法的研究论文。
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arXiv:2607.06879v1 Announce Type: cross Abstract: Best-arm identification is a canonical model for data-driven decision-making, but in many applications each reward observation is costly. Motivated by the growing availability of cheap predictions from machine learning and large l…
Best-arm identification is a canonical model for data-driven decision-making, but in many applications each reward observation is costly. Motivated by the growing availability of cheap predictions from machine learning and large language models, we study fixed-confidence best-arm…