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（BO）在工程应用中的实际使用提出了特殊要求：一方面需要高采样效率，另一方面需要找到鲁棒解。我们针对对抗鲁棒性问题展开研究，该场景下所有参数在优化过程中是可控的，但在实际应用时，其子集可能变得不可控甚至受到逆向扰动。为此，我们开发了一种基于信息论的高效采集函数，称为鲁棒熵搜索（RES）。我们通过合成数据与真实数据的实验实证证明了该方法的优势。结果表明，RES能够可靠地找到鲁棒最优解，其性能优于现有最先进算法。