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.
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Communication dans un congrès
Learning and Intelligent Optimization: 9th International Conference, LION 9. Revised Selected Papers, Jan 2015, Lille, France. 8994, 2015, Lecture Notes in Computer Science. 〈http://link.springer.com/chapter/10.1007/978-3-319-19084-6_28〉. 〈10.1007/978-3-319-19084-6_28〉
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https://hal.archives-ouvertes.fr/hal-01078003
Contributeur : Mickaël Binois <>
Soumis le : vendredi 20 février 2015 - 12:43:30
Dernière modification le : mardi 23 octobre 2018 - 14:36:09
Document(s) archivé(s) le : jeudi 28 mai 2015 - 16:17:33

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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. 8994, 2015, Lecture Notes in Computer Science. 〈http://link.springer.com/chapter/10.1007/978-3-319-19084-6_28〉. 〈10.1007/978-3-319-19084-6_28〉. 〈hal-01078003v2〉

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