Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences

Abstract:This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely studied and used in machine learning, statistics, and numerical analysis. We study connections and equivalences for fundamental topics such as regression, interpolation, numerical integration, distributional discrepancies, and statistical dependence, as well as sample path properties of Gaussian processes. A unifying perspective for these equivalences is established, based on the equivalence between the Gaussian Hilbert space and the RKHS. The monograph serves as a basis to bridge many other methods based on Gaussian processes and reproducing kernels, which are developed in parallel by the two research communities.

Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR); Statistics Theory (math.ST)

arXiv:2506.17366 [stat.ML]

(or arXiv:2506.17366v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2506.17366 Focus to learn more arXiv-issued DOI via DataCite

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