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
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Jul 15, 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
AuthorNicolas Schreuder
AuthorErnesto De Vito
AuthorLorenzo Rosasco
AuthorResearch Topics & Keywords
Nonparametric Statistics
Research AreaCitation 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{JMLR:v26:23-1551,
author = {Antoine Chatalic and Nicolas Schreuder and Ernesto De Vito and Lorenzo Rosasco},
title = {Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling},
journal = {Journal of Machine Learning Research},
year = {2025},
volume = {26},
number = {101},
pages = {1--55},
url = {http://jmlr.org/papers/v26/23-1551.html}
}