Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

• URL: http://arxiv.org/abs/2006.04296v1
• Date: Mon, 8 Jun 2020 00:28:41 GMT
• Title: Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
• Authors: Julian Berk, Sunil Gupta, Santu Rana and Svetha Venkatesh
• Abstract summary: 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.
• Score: 60.93091603232817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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