IJCAI 2020poster0 citations
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh
Abstract
In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.
Machine Learning: Bayesian OptimizationMachine Learning: Cost-Sensitive Learning
BibTeX
@inproceedings{ijcai2020p316,
title = {Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation},
author = {Berk, Julian and Gupta, Sunil and Rana, Santu and Venkatesh, Svetha},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {2284--2290},
year = {2020},
month = {7},
note = {Main track},
doi = {10.24963/ijcai.2020/316},
url = {https://doi.org/10.24963/ijcai.2020/316},
}