Bayesian optimization with exponential convergence
Name
Lozano-Perez_Bayesian optimization.pdf
Size
1 MB
Format
Adobe PDF
Checksum (MD5)
e9d34886d67efe99c6fe990a57434419
Author(s)
Kawaguchi, Kenji
Kaelbling, Leslie P
Lozano-Perez, Tomas
Date Issued
December 2015
Journal
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Publisher
Neural Information Processing Systems Foundation,
Citation
Kawaguchi, Kenji, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. "Bayesian Optimization with Exponential Convergence." Advances in Neural Information Processing Systems 28 (NIPS 2015), 7-12 December, 2015, Montreal, Canada, Neural Information Processing Systems Foundation, 2015. © 2015 Neural Information Processing Systems Foundation
Version
Final published version
Abstract
This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence requires access to the delta-cover sampling, which was considered to be impractical. Our approach eliminates both requirements and achieves an exponential convergence rate.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/5715-bayesian-optimization-with-exponential-convergence
