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

arXiv:2609.15555 · cs.LG · Submitted 2026-09-14 · Read on arXiv

cs.LG

Submitted: 2026-09-14

Updated: 2026-09-14

Code: https://github.com/yhong123/bo-pro-c

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 46× in this paper · explained in 3 episodes
• Computational overhead — 44× in this paper · explained in 2 episodes
• Acquisition Function — 34× in this paper · explained in 1 episode
• Calibration — 28× in this paper · explained in 7 episodes
• Benchmark — 22× in this paper · explained in 2 episodes
• Bayesian Optimization — 8× in this paper · explained in 3 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Objective Function — 6× in this paper · explained in 2 episodes
• Uncertainty Quantification — 4× in this paper · explained in 8 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• A Tutorial on Bayesian Optimization
• Transductive Log Opinion Pool of Gaussian Process Experts

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