Joint optimization and variable selection of high-dimensional Gaussian processes

B. Chen, R.M. Castro, A. Krause

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

27 Citations (Scopus)

Abstract

Maximizing high-dimensional, non-convex functions through noisy observations is a notoriously hard problem, but one that arises in many applications. In this paper, we tackle this challenge by modeling the unknown function as a sample from a high-dimensional Gaussian process (GP) distribution. Assuming that the unknown function only depends on few relevant variables, we show that it is possible to perform joint variable selection and GP optimization. We provide strong performance guarantees for our algorithm, bounding the sample complexity of variable selection, and as well as providing cumulative regret bounds. We further provide empirical evidence on the effectiveness of our algorithm on several benchmark optimization problems.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on achine Learning (ICML 2012, Edinburgh, Scotland, UK, June 26-July 1, 2012)
Pages1423-1430
Volume2
Publication statusPublished - 2012
Event29th International Conference on Machine Learning (ICML 2009) - Montrel, Canada
Duration: 14 Jun 200918 Jun 2009
Conference number: 29

Conference

Conference29th International Conference on Machine Learning (ICML 2009)
Abbreviated titleICML 2009
CountryCanada
CityMontrel
Period14/06/0918/06/09
Other29th International Conference on Machine Learning

Cite this

Chen, B., Castro, R. M., & Krause, A. (2012). Joint optimization and variable selection of high-dimensional Gaussian processes. In Proceedings of the 29th International Conference on achine Learning (ICML 2012, Edinburgh, Scotland, UK, June 26-July 1, 2012) (Vol. 2, pp. 1423-1430)