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Positively Weighted Kernel Quadrature via Subsampling
Satoshi Hayakawa · Harald Oberhauser · Terry Lyons

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #722

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 Information

Satoshi Hayakawa (University of Oxford)
Harald Oberhauser (University of Oxford)
Terry Lyons (University of Oxford)

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