5 papers
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…
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…
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…
Adaptive Kernel Methods
Tamás Dózsa, Andrea Angino, Zoltán Szabó +2
Kernel methods approximate nonlinear maps in a data-driven manner by projecting the target map onto a finite-dimensional Hilbert space called the solution space. Traditionally, thi…
Generalized rational Prony and Bernoulli methods
Tamás Dózsa, Matthias Voigt, Zoltán Szabó +2
The generalized operator-based Prony method is an important tool for describing signals which can be written as finite linear combinations of eigenfunctions of certain linear opera…