arXiv Machine Learning July 28, 2026 By Jose Cribeiro-Ramallo, Florian Kalinke, Zolt\'an Szab\'o

Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD

Read the original on arXiv Machine Learning →

arXiv:2607. 24235v1 Announce Type: cross Abstract: Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications in two-sample, goodness-of-fit, and independence testing, among others.

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