寻找在部署后最抗扰动的满意解，提升设计鲁棒性。
Maximally Robust Satisficing Bayesian Optimization
- 基于输入可控但部署后会扰动的假设，优化满意解的鲁棒性。
- 在多个满意解中优先选择对最大扰动仍稳定的解。
- 适合对可靠性要求高的工程设计场景。
许多设计任务可视为黑箱函数优化问题，利用贝叶斯优化可在最少试验次数下找到理想设计。然而，实际中往往无需最优解，只需满足特定性能要求即可，例如材料在预期用途下足够耐用。通常存在多个满足条件的解，构成函数的超水平集。关键问题在于如何在这些解中选择最优。本文提出，部署时可能发生的输入扰动应作为优选标准，并引入一种贝叶斯优化方法，高效寻找对最大扰动具有最强鲁棒性的满意解。与以往工作不同，本方法假设优化阶段输入可精确控制，但部署后会受到扰动。
原文摘要 · Abstract (English)
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.
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