The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of adversarial robustness, where all parameters are controllable during the optimization process, but a subset of them is uncontrollable or even adversely perturbed at the time of application. To this end, we develop an efficient information-based acquisition function that we call Robust Entropy Search (RES). We empirically demonstrate its benefits in experiments on synthetic and real-life data. The results showthat RES reliably finds robust optima, outperforming state-of-the-art algorithms.

翻译：贝叶斯优化在工程应用中的实际运用提出了特殊要求：一方面需要高采样效率，另一方面要求找到稳健解。本文针对对抗性鲁棒场景展开研究，该场景下所有参数在优化过程中均可控，但在实际应用时部分参数不可控甚至受到不利扰动。为此，我们提出一种基于信息论的高效采集函数，称为鲁棒熵搜索。通过在合成数据与真实数据上的实验，我们实证验证了该方法的优势。结果表明，鲁棒熵搜索能够可靠地找到鲁棒最优解，其性能优于现有先进算法。