Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
For researchers in multi-objective decision-making, this provides the first anytime algorithm for Pareto set identification, enabling dynamic stopping and confidence monitoring.
The paper introduces the first anytime Multi-Objective Multi-Armed Bandit algorithm for Pareto Set Identification, demonstrating superior performance over fixed-budget baselines in synthetic environments and practical utility in molecular discovery, with theoretical guarantees of asymptotic correctness.
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.