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New Study Examines Regularization Versus Convolution in Kernel-Based Hypothesis Testing

This story is from 2026-11-06. It is preserved in the archive; the latest stories are on the live feed.

A paper on arXiv analyzes the maximum mean discrepancy (MMD) test, a popular nonparametric test on general domains, comparing regularization and convolution approaches to its optimality.

Read the full story at arXiv stat.ML ↗

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  1. 2026-11-06 05:00 · arXiv stat.ML
    Regularization vs. Convolution in Kernel-Based Hypothesis Testing

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