arXiv cs.AI· Lennert Saerens, Bram Silue, Eleni Litsa, Peter Vrancx, Pieter Libin·· 15 小时前AI 评分24
TTPFTS：面向多目标多臂老虎机的贝叶斯随时帕累托集识别算法
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
AI 导读
研究者提出 TTPFTS，这是首个面向多目标多臂老虎机（MOMAB）帕累托集识别的贝叶斯随时算法。在合成环境与超大规模按需合成分子库的多目标多智能体分子发现任务中，TTPFTS 均优于固定置信度、固定预算的 SOTA 方法及行业基线。研究还提出一种估计预测帕累托集置信度的不确定性量化指标，并给出该场景下首个随时 MOMAB 帕累托集识别算法的有限时间理论分析。
正文 · 原文
Abstract:Identifying Pareto optimal solutions is critical to support multi-objective decision-making. In this work, we present Top-Two Pareto Front Thompson Sampling (TTPFTS), the first anytime algorithm for Pareto Set Identification in Multi-Objective Multi-Armed Bandits (MOMABs), taking a Bayesian approach. We benchmark TTPFTS against state-of-the-art fixed-confidence and fixed-budget Pareto Set Identification algorithms on synthetic environments. Next, we demonstrate its practical utility in a challenging real-world multi-objective multi-agent molecular discovery setting by efficiently exploring an ultra-large synthesis-on-demand molecular library, outperforming industry-standard and state-of-the-art baselines. Furthermore, we introduce a novel uncertainty quantification metric that estimates our algorithm's confidence in the predicted Pareto set. We demonstrate that this metric effectively proxies true performance, yielding a robust methodology for monitoring learning progress in complex settings. Finally, we complement these empirical findings with the first finite-time theoretical analysis of an anytime MOMAB Pareto Set Identification algorithm.
| Comments: | 46 pages, 15 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.18785 [cs.LG] |
| (or arXiv:2606.18785v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18785 arXiv-issued DOI via DataCite |
Submission history
From: Lennert Saerens [view email]
[v1]
Wed, 17 Jun 2026 07:56:51 UTC (10,175 KB)
[v2]
Mon, 26 Oct 2026 08:56:22 UTC (10,743 KB)
来源：arXiv cs.AI · arxiv.org