Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
This addresses constrained multi-objective optimization for applications like drug discovery, offering a more efficient alternative to heuristic-based methods, though it appears incremental as it builds on existing Bayesian optimization frameworks.
The paper tackles the problem of multi-objective Bayesian optimization with constraints, such as safety thresholds in drug discovery, by proposing the COMBOO algorithm, which balances constraint learning and optimization, and demonstrates efficacy through theoretical analysis and empirical results on synthetic benchmarks and real-world applications.
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.