6 papers
Unbounded Characteristic and Universal Kernels
Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó
Kernel methods are among the most powerful tools in machine learning and statistics, with a large number of successful applications. Their immense success stems from the flexible f…
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…
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…
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…