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

• URL: http://arxiv.org/abs/2609.15555v1
• Date: Mon, 14 Sep 2026 13:37:35 GMT
• Title: Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration
• Abstract summary: This paper introduces BO-pro-c, a Bayesian optimisation algorithm that uses GP-pro-c as its surrogate model.<n> Experimental results suggest BO-pro-c maintains competitive optimisation performance while achieving a 0.9% reduction in simple regret.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 algorithm that uses GP-pro-c as its surrogate model, and evaluate its performance across a diverse range of BO settings. Experimental results suggest that BO-pro-c maintains competitive optimisation performance while achieving a 0.9% reduction in simple regret and a 39.4% reduction in computational overhead relative to a BO algorithm based on a single global GP model.

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