- Conference Paper
We consider the problem of robust optimization within the well-established Bayesian Optimization (BO) framework.While BO is intrinsically robust to noisy evaluations of the objective function, standard approaches do not consider the case of uncertainty about the input parameters.In this paper, we propose Noisy-Input Entropy Search (NES), a novel information-theoretic acquisition function that is designed to find robust optima for problems with both input and measurement noise.NES is based on the key insight that the robust objective in many cases can be modeled as a Gaussian process, however, it cannot be observed directly.We evaluate NES on several benchmark problems from the optimization literature and from engineering.The results show that NES reliably finds robust optima, outperforming existing methods from the literature on all benchmarks. Show more
Book titleProceedings of the 23rd International Conference on Artificial Intelligence and Statistics
Journal / seriesProceedings of Machine Learning Research
Pages / Article No.
Organisational unit09563 - Zeilinger, Melanie / Zeilinger, Melanie
157601 - Safety and Performance for Human in the Loop Control (SNF)
NotesDue to the Corona virus (COVID-19) the conference was conducted virtually.
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