JMLR

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

Authors
Antoine Chatalic Nicolas Schreuder Ernesto De Vito Lorenzo Rosasco
Research Topics
Nonparametric Statistics
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Paper Information
  • Journal:
    Journal of Machine Learning Research
  • Added to Tracker:
    Jul 30, 2025
Abstract

In this work we consider the problem of numerical integration, i.e., approximating integrals with respect to a target probability measure using only pointwise evaluations of the integrand. We focus on the setting in which the target distribution is only accessible through a set of $n$ i.i.d. observations, and the integrand belongs to a reproducing kernel Hilbert space. We propose an efficient procedure which exploits a small i.i.d. random subset of $m[abs][pdf][bib]        [code]©JMLR2025. (edit,beta)

Author Details
Antoine Chatalic
Author
Nicolas Schreuder
Author
Ernesto De Vito
Author
Lorenzo Rosasco
Author
Research Topics & Keywords
Nonparametric Statistics
Research Area
Citation Information
APA Format
Antoine Chatalic , Nicolas Schreuder , Ernesto De Vito & Lorenzo Rosasco . Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. Journal of Machine Learning Research .
BibTeX Format
@article{paper136,
  title = { Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling },
  author = { Antoine Chatalic and Nicolas Schreuder and Ernesto De Vito and Lorenzo Rosasco },
  journal = { Journal of Machine Learning Research },
  url = { https://www.jmlr.org/papers/v26/23-1551.html }
}
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