arXiv Machine Learning
Sep 15

Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration

The paper introduces BO-pro-c, a Bayesian optimisation algorithm that employs a product-of-experts Gaussian process (GP-pro-c) as its surrogate model. GP-pro-c combines multiple local GP experts to improve uncertainty quantification, reduce computational cost, and preserve global correlations, addressing the cubic complexity of single global GP models. Experiments show that BO-pro-c achieves competitive optimisation performance with a 0.9% lower simple regret and a 39.4% reduction in computational overhead compared to a single‑global‑GP baseline.

By Yean Hoon Ong