Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

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.

Authors

• Julian Berk
• Sunil Gupta Deakin University, Australia
• Santu Rana
• Svetha Venkatesh Deakin University

Keywords

• Machine Learning: Bayesian Optimization
• Machine Learning: Cost-Sensitive Learning