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News/Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
arxiv
PublishedJuly 10, 2026 at 4:00 AM
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Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

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Publisher summary· verbatim

arXiv:2603.16481v3 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. In this domain, kernel methods such as Gaussian process regression ar

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