HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

A warped kernel improving robustness in Bayesian optimization via random embeddings

Abstract : This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel. The proposed warping, that relies on elementary geometric considerations, allows mitigating the drawbacks of the high extrinsic dimensionality while avoiding the algorithm to evaluate points giving redundant information. It also alleviates constraints on bound selection for the embedded domain, thus improving the robustness, as illustrated with a test case with 25 variables and intrinsic dimension 6.
Complete list of metadata

Contributor : Mickaël Binois Connect in order to contact the contributor
Submitted on : Friday, February 20, 2015 - 12:43:30 PM
Last modification on : Sunday, April 4, 2021 - 10:22:06 AM
Long-term archiving on: : Thursday, May 28, 2015 - 4:17:33 PM


Files produced by the author(s)



Mickaël Binois, David Ginsbourger, Olivier Roustant. A warped kernel improving robustness in Bayesian optimization via random embeddings. Learning and Intelligent Optimization: 9th International Conference, LION 9. Revised Selected Papers, Jan 2015, Lille, France. ⟨10.1007/978-3-319-19084-6_28⟩. ⟨hal-01078003v2⟩



Record views


Files downloads