English(EN) Balanced Sampling in Stochastic Multi-objective Bandits

新的老虎机采样方法平衡多目标权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一种在随机多目标老虎机中进行平衡采样的新方法，解决了未知帕累托最优臂的挑战。所提出的方法使用 Kullback–Leibler 散度来衡量不公平性，并引入了自适应前两个采样，该采样在不预先承诺估计的帕累托集的情况下指导采样。该方法在期望不公平性和方差方面实现了理论界限，数值研究支持其在实践中的见解。 AI

影响为多目标老虎机问题引入了一种新颖的采样策略，有可能改善在线广告等领域的决策。

排序理由学术论文，详细介绍了一种针对特定机器学习问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Mengfan Xu · 2026-10-27 04:00

随机多目标赌博机中的平衡采样

arXiv:2610.35551v1 Announce Type: cross Abstract: In stochastic multi-objective bandits, Pareto optimal arms may represent different trade-offs among objectives. Balanced sampling across them is important in practical settings such as online advertising. Yet, there seems to be li…

本文如何被排名

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随机多目标赌博机中的平衡采样

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