Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration
Bayesian optimisation (BO) typically relies on a single global Gaussian process (GP) model as its surrogate model. However, GP regression has cubic computational complexity in the number of training data points, limiting its applicability to large-scale optimisation problems. The product-of-experts Gaussian process model with uncertainty calibration (GP-pro-c) mitigates this limitation by combining multiple local GP experts, enabling improved uncertainty quantification, reduced computational cost, and preservation of global correlations. Despite these desirable properties, the use of GP-pro-c in BO has not been thoroughly studied. This paper introduces BO-pro-c, a Bayesian optimisation al...
| Authors | Yean Hoon Ong |
|---|---|
| Primary category | cs.LG |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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