发表机构
University of Helsinki; ASM International N.V.（赫尔辛基大学; 阿斯麦尔公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究设计任务中黑箱函数优化问题，提出一种贝叶斯优化方法，该方法能有效找到对最大扰动具有鲁棒性的满意解，且假设优化时输入可控，部署后会受扰。
AI 中文摘要
许多设计任务可归结为黑箱函数优化，能利用贝叶斯优化以最少试验次数找到理想设计。但通常无需最优解，足够好的解即可，且存在多个满意解。本文解释为何对部署时可能出现的输入扰动的鲁棒性是个好标准，并引入一种贝叶斯优化方法，能有效找到对最大扰动具有鲁棒性且令人满意的解。与以往工作不同，本文假设优化期间输入可精确控制，部署后会受扰动。
英文摘要
Many design tasks can be cast as black-box function optimization, enabling use of Bayesian optimization to find an ideal design with minimal number of trials. However, often we do not actually need the optimum but instead a sufficiently good solution is enough, for instance a material that is durable enough for its intended use. In most cases there are multiple satisfactory solutions, forming a superlevel set of the function, raising a key question of which one to prefer. We answer this by explaining why robustness to input perturbations that may occur when the solution is deployed is a good criterion and by introduce a Bayesian optimization method that efficiently finds satisficing solutions that are robust to maximally large perturbations. In contrast to previous works, we assume the inputs can be accurately controlled during optimization, but will be perturbed after the deployment.
CommentsAccepted to the Conference on Uncertainty in Artificial Intelligence (UAI) 2026
