English(EN) Multiple-Choice Secretary Problem with Predictions

改进算法，通过预测选择顶尖候选人

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员改进了多选择秘书问题的Learned Kleinberg算法，该问题旨在随机顺序到达的候选人中选择最多k名候选人以最大化其总价值。改进后的算法实现了至少 \includegraphics[width=0.08\textwidth]{max}0,1-\includegraphics[width=0.08\textwidth]{min}\{32/\sqrt{k},4\eps\}} 的竞争比，消除了先前分析中的对数因子。这意味着，通过精确预测，该算法可以实现最优的离线结果，而无需考虑到达顺序。 AI

影响提高了具有预测能力的选拔算法的理论性能。

排序理由详细介绍算法改进的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jing Yuan, Qi Cai, Xin Gao, Shaojie Tang · 2026-11-14 05:00

带预测的多项选择秘书问题

arXiv:2611.14651v1 Announce Type: cross Abstract: We study the multiple-choice secretary problem, in which candidates arrive in uniformly random order and the goal is to select at most $k$ candidates to maximize their total value. We consider a setting where predictions of indivi…

本文如何被排名

报道来源 [1]

带预测的多项选择秘书问题

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