Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization

Lukas Fröhlich, Edgar Klenske, Julia Vinogradska, Christian Daniel, Melanie Zeilinger
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2262-2272, 2020.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-frohlich20a, title = {Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization}, author = {Fr\"ohlich, Lukas and Klenske, Edgar and Vinogradska, Julia and Daniel, Christian and Zeilinger, Melanie}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {2262--2272}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/frohlich20a/frohlich20a.pdf}, url = {https://proceedings.mlr.press/v108/frohlich20a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization %A Lukas Fröhlich %A Edgar Klenske %A Julia Vinogradska %A Christian Daniel %A Melanie Zeilinger %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-frohlich20a %I PMLR %P 2262--2272 %U https://proceedings.mlr.press/v108/frohlich20a.html %V 108 %X 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.
APA
Fröhlich, L., Klenske, E., Vinogradska, J., Daniel, C. & Zeilinger, M.. (2020). Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:2262-2272 Available from https://proceedings.mlr.press/v108/frohlich20a.html.

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