首个随时算法 TTPFTS：多目标多臂老虎机中的 Pareto 前沿识别
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
AI 导读
研究者提出 Top-Two Pareto Front Thompson Sampling（TTPFTS），这是多目标多臂老虎机 Pareto 前沿识别的首个随时（anytime）贝叶斯算法。
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