Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
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Abstract (arXiv)
This paper extends safety guarantees for multi-task Bayesian optimization with uncertain co-regionalization matrices from intrinsic co-regionalization models to linear models of co-regionalization. The latter allows for more flexible modeling of the inter-task correlations by composing multiple features. We derive uniform error bounds for vector-valued functions sampled from a Gaussian process with a linear model of co-regionalization kernel. Furthermore, we show the potential performance gains of linear models of co-regionalization in a numerical comparison on a safe multi-task Bayesian optimization benchmark.
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References · 12
- 2017On Kernelized Multi-armed Banditscited by 348
- 2018Meta-Learning: A Surveycited by 254
- 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 201
- 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 131
- 2020PACOH: Bayes-Optimal Meta-Learning with PAC-Guaranteescited by 85
- 2019Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspacescited by 73
- 2019Uniform Error Bounds for Gaussian Process Regression with Application to Safe Controlcited by 64
- 2021Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applicationscited by 17
- 2023Towards Safe Multi-Task Bayesian Optimizationcited by 5
- 2023MALIBO: Meta-learning for Likelihood-free Bayesian Optimizationcited by 2
- 2022Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulationcited by 1
- 2025An Analysis of Safety Guarantees in Multi-Task Bayesian Optimizationcited by 1
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