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Munteanu/etal/2019a: A Framework for Bayesian Optimization in Embedded Subspaces

Bibtype Inproceedings
Bibkey Munteanu/etal/2019a
Author Munteanu, Alexander and Nayebi, Amin and Poloczek, Matthias
Title A Framework for {B}ayesian Optimization in Embedded Subspaces
Booktitle Proceedings of the 36th International Conference on Machine Learning (ICML)
Series Proceedings of Machine Learning Research
Volume 97
Pages 4752--4761
Address Long Beach, California, USA
Publisher PMLR
Abstract We present a theoretically founded approach for high-dimensional Bayesian optimization based on low-dimensional subspace embeddings. We prove that the error in the Gaussian process model is bounded tightly when going from the original high-dimensional search domain to the low-dimensional embedding. This implies that the optimization process in the low-dimensional embedding proceeds essentially as if it were run directly on an unknown active subspace of low dimensionality. The argument applies to a large class of algorithms and GP models, including non-stationary kernels. Moreover, we provide an efficient implementation based on hashing and demonstrate empirically that this subspace embedding achieves considerably better results than the previously proposed methods for high-dimensional BO based on Gaussian matrix projections and structure-learning.
Month 06
Year 2019
Projekt SFB876-C4
Url http://proceedings.mlr.press/v97/nayebi19a.html
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