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Positively Weighted Kernel Quadrature via Subsampling by Hayakawa, Satoshi; Oberhauser, Harald; Lyons, Terry is a scholarly article available to read on EtoBox.

What is Positively Weighted Kernel Quadrature via Subsampling about?

We study kernel quadrature rules with convex weights. Our approach combines the spectral properties of the kernel with recombination results about point measures. This results in effective algorithms that construct convex quadrature rules using only access to i.i.d. samples from the underlying measure and evaluation of the kernel and that result in a small worst-case error. In addition to our theoretical results and the benefits resulting from convex weights, our experiments indicate that this construction can compete with the optimal bounds in well-known examples.

Author

Hayakawa, Satoshi; Oberhauser, Harald; Lyons, Terry

Published

2021

Language

EN