发表机构
Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种相关性感知的解耦多目标贝叶斯优化方法，利用多任务高斯过程联合建模并选择最优子集评估，在理论上保证一致性并实证超越现有基线。
AI 中文摘要
使用高斯过程（GP）代理的多目标贝叶斯优化（MOBO）是解决多目标优化问题的一种样本高效方法。在MOBO中，贝叶斯决策理论采集函数指导新候选输入的自适应选择，并依次评估目标和约束以更新代理模型。现有方法为目标和约束维护独立的GP模型，新观测以耦合方式评估所有目标和约束。然而，目标和约束通常包含内在相关性，若加以利用，可以实现解耦评估，即每轮仅评估其中一部分。我们提出了一种新方法，利用多任务GP模型联合学习所有目标和约束，并提出一种总相关性度量，即使在均匀评估成本下，也能识别每轮应评估的最优目标和约束子集。理论上，我们证明了尽管解耦，我们的采集策略是渐近一致的，并且我们提出的解耦子集选择规则在温和条件下最大化关于未评估任务的期望后验熵减少。实证上，我们展示了我们的方法在现有技术水平上优于耦合和解耦基线。
英文摘要
Multiobjective Bayesian optimization (MOBO) with Gaussian process (GP) surrogates is a sample efficient approach to solving multiobjective optimization problems. In MOBO, a Bayesian decision theoretic acquisition function guides the adaptive selection of new candidate inputs, on which objectives and constraints are evaluated to update the surrogate model sequentially. Existing approaches maintain independent GP models for the objectives and constraints, with new observations evaluating all objectives and constraints in a coupled fashion. However, the objectives and constraints often contain inherent correlations which, if exploited, can enable ${decoupled}$ evaluations where only a subset of them are evaluated at each round. We present a new approach that leverages a multitask GP model to jointly learn all objectives and constraints, and propose a total correlation metric that enables identifying an ${optimal}$ subset of objectives and constraints to be evaluated at every round, even under uniform evaluation costs. Theoretically, we show that our acquisition policy is asymptotically consistent despite decoupling and that our proposed decoupled subset selection rule maximizes the expected posterior entropy reduction about unevaluated tasks under mild conditions. Empirically, we show that our approach outperforms coupled and decoupled baselines in the state of the art.
Comments30 pages, 15 figures
