7 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Nyström Kernel Stein Discrepancy Tests
Florian Kalinke, Zoltán Szabó, Bharath K. Sriperumbudur
Kernel Stein discrepancy (KSD) is among the most popular goodness-of-fit (GoF) measures on general domains with a large number of successful deployments. One of the main applicatio…
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…
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…
Nyström Kernel Stein Discrepancy
Florian Kalinke, Zoltan Szabo, Bharath K. Sriperumbudur
Kernel methods underpin many of the most successful approaches in data science and statistics, and they allow representing probability measures as elements of a reproducing kernel…