NeurIPS 2015poster135 citations
Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
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.
BibTeX
@inproceedings{NIPS2015_0ebcc77d,
author = {Kawaguchi, Kenji and Kaelbling, Leslie Pack and Lozano-P\'{e}rez, Tom\'{a}s},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Bayesian Optimization with Exponential Convergence},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/0ebcc77dc72360d0eb8e9504c78d38bd-Paper.pdf},
volume = {28},
year = {2015}
}