解决多目标优化中带约束的实验设计问题，提升样本效率。
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
- 通过乐观约束估计平衡多目标优化与可行域搜索
- 在合成与真实场景中均实现更高样本效率
- 适合需安全阈值的药物发现等实际应用
多目标贝叶斯优化广泛应用于科学实验设计，如药物发现和超参数优化。实践中，监管或安全要求常对实验结果的某些属性施加阈值限制。以往工作主要聚焦于约束单目标优化或受限下的主动搜索，现有约束多目标算法多依赖启发式方法和近似，影响样本效率分析。本文提出新型约束多目标贝叶斯优化算法 COMBOO，通过乐观约束估计，平衡多个未知量上水平集的主动学习与可行域内的多目标优化。我们提供了理论分析与实证证据，证明该方法在多种合成基准和真实应用中的有效性。
原文摘要 · Abstract (English)
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.
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