Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911699645235200 |
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| author | Lübsen, Jannis Eichler, Annika |
| author_facet | Lübsen, Jannis Eichler, Annika |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13302 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization Lübsen, Jannis Eichler, Annika Machine Learning Systems and Control 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. |
| title | Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2605.13302 |