Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
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Computer Science > Machine Learning
Title:Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
Abstract:Identifying Pareto optimal solutions is critical to support multi-objective decision-making. We introduce the first anytime Multi-Objective Multi-Armed Bandit algorithm for the Pareto Set Identification problem, taking a Bayesian approach: Top-Two Pareto Front Thompson Sampling (TTPFTS). We benchmark TTPFTS against state-of-the-art fixed-budget Pareto Set Identification algorithms on synthetic environments. Next, we demonstrate its practical utility in a challenging multi-objective molecular discovery setting by efficiently exploring an ultra-large synthesis-on-demand molecular library. 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 a theoretical proof of the algorithm's asymptotic correctness.
| Comments: | 26 pages, 13 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.18785 [cs.LG] |
| (or arXiv:2606.18785v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18785
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Lennert Saerens [view email][v1] Wed, 17 Jun 2026 07:56:51 UTC (10,175 KB)
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