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Safe Bayesian Optimization for Uncertain Correlations Mat...
Jannis L\"ub · 2026-05-14 · via cs.LG updates on arXiv.org

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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)