使用不确定性校准的专家乘积高斯过程模型进行贝叶斯优化
Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration
- University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出BO-pro-c，一种采用不确定性校准的专家乘积高斯过程模型作为代理的贝叶斯优化算法，在保持竞争性能的同时，显著降低计算开销并减少简单遗憾。
AI中文摘要：
贝叶斯优化（BO）通常依赖单一全局高斯过程（GP）模型作为其代理模型。然而，GP回归在训练数据点数量上具有三次方的计算复杂度，限制了其在大规模优化问题中的适用性。具有不确定性校准的专家乘积高斯过程模型（GP-pro-c）通过组合多个局部GP专家来缓解这一限制，从而改进不确定性量化、降低计算成本并保持全局相关性。尽管具有这些理想特性，GP-pro-c在BO中的使用尚未得到深入研究。本文介绍了BO-pro-c，一种使用GP-pro-c作为其代理模型的贝叶斯优化算法，并在多种BO设置下评估其性能。实验结果表明，与基于单一全局GP模型的BO算法相比，BO-pro-c在保持竞争性优化性能的同时，实现了简单遗憾减少0.9%和计算开销减少39.4%。
英文摘要：
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.
