  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.
