Abstract:This paper extends safety guarantees for multi-task Bayesian optimization with uncertain correlation matrices from intrinsic co-reginalization models to linear models of co-reginalization. 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-reginalization kernel. Furthermore, we show the potential improvement of performance using linear models of co-reginalization in a numerical comparison on a safe multi-task Bayesian optimization benchmark.
| Comments: | Accepted at IFAC WC26 |
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.13302 [cs.LG] |
| (or arXiv:2605.13302v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13302 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jannis Lübsen [view email]
[v1]
Wed, 13 May 2026 10:14:43 UTC (839 KB)