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Gaussian Processes and RKHS Connections Explored in New Monograph

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This monograph explores the connections and equivalences between Gaussian Processes (GPs) and Reproducing Kernel Hilbert Spaces (RKHS), two prominent kernel-based approaches in machine learning and statistics. It establishes a unifying perspective based on the equivalence between Gaussian Hilbert spaces and RKHS, bridging parallel developments in probabilistic and non-probabilistic methods. The work covers fundamental topics such as regression, interpolation, and numerical integration, aiming to foster collaboration between the two research communities. AI

IMPACT Provides a theoretical framework that could unify disparate research in machine learning and statistics.

RANK_REASON The item is a research paper published on arXiv detailing theoretical connections between two machine learning concepts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gaussian Processes and RKHS Connections Explored in New Monograph

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The item is a research paper published on arXiv detailing theoretical connections between two machine learning concepts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur ·

    Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences

    arXiv:2506.17366v2 Announce Type: replace 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 …

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