Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation

Diantong Li, Fengxue Zhang, Chong Liu, Yuxin Chen

TL;DR: We propose a sample-efficient constrained multi-objective Bayesian optimization algorithm that balances active learning of level sets with optimization in feasible regions, supported by theoretical justification and empirical validation.

Abstract:

Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional thresholds on certain attributes of the experimental outcomes. Previous work has primarily focused on constrained single-objective optimization tasks or active search under constraints. The existing constrained multi-objective algorithms address the issue with heuristics and approximations, posing challenges to the analysis of the sample efficiency.
We propose a novel constrained multi-objective Bayesian optimization algorithm **COMBOO** that balances active learning of the level-set defined on multiple unknowns with multi-objective optimization within the feasible region. We provide both theoretical analysis and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.

Submission Number: 135