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stat.ML2026
Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD
Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó
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…
stat.ML2026
Kernel Integrated : A Measure of Dependence
Pouya Roudaki, Shakeel Gavioli-Akilagun, Florian Kalinke +2
We introduce kernel integrated , a new measure of statistical dependence that combines the local normalization principle of the recently introduced integrated with the f…
stat.ML2025
The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
Jose Cribeiro-Ramallo, Agnideep Aich, Florian Kalinke +2
Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of o…