math.STOct 7, 2026

Unbounded Characteristic and Universal Kernels

Authors: Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó

Organizations: Karlsruhe Institute of Technology · London School of Economics

Abstract

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 function class associated to each kernel---its reproducing kernel Hilbert space (RKHS)---which facilitates statistical analysis, as well as from their computational tractability and applicability to many domains. Multiple notions (such as characteristic, LpL_p-universal, and integrally strictly positive definite) capture the expressivity of kernels and their RKHSs and play a key role in understanding the statistical properties of kernel methods; these concepts and their relations are well-understood for bounded kernels. Even though unbounded kernels have received significant attention over the past decade (for instance, in the construction of kernel-based discrepancy and dependence measures such as the maximum mean discrepancy, the Hilbert-Schmidt independence criterion, and the kernel Stein discrepancy), surprisingly little is known about the relations of these notions in the unbounded case. In the present paper we tackle this severe bottleneck, establishing their relations under mild assumptions.

Figures & tables

Explore similar work

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

    Jul 27, 2026Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán SzabóMaximum Mean DiscrepancyMinimax Estimation

  2. Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding

    May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge RegressionKernel Regression