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.

通过开发高效的信息获取函数 Robust Entropy Search（RES），我们在合成数据和真实数据的实验中实证其优点，并展示了 RES 相对于最先进算法的优越性，从而解决了工程应用中贝叶斯优化（BO）的高采样效率和找到稳健解决方案的特殊要求，特别是在参数完全可控但在应用时部分参数不可控或甚至被干扰的对抗鲁棒性情况下。