Kernel Stein Discrepancy

Also known as KSD

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8 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Task-Calibrated Random Features via Influence-Based Error Cancellation
Nov 4, 2026Shinto EguchiKernel Stein Discrepancy Random Feature Methods
Generative SDEs with Trainable Diffusion and Density-Based Losses
Oct 30, 2026Angxiu NiKernel Stein Discrepancy Generative Modeling
Department of Mathematics, University of California, Irvine, USA
Task-aware Kernel Stein certificates for Rényi differential privacy
Oct 16, 2026Leoni Carla Wirth, Tara Trauthwein, Gesine ReinertKernel Stein Discrepancy Rényi Differential Privacy
Department of Mathematics, Imperial College London, London SW7 2AZ, UK · Institute for Mathematical Stochastics, University of Münster, Münster, Germany · Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
Conditional Kernel Stein Discrepancy
Oct 8, 2026Federico Matteucci, Florian KalinkeKernel Stein Discrepancy Goodness-of-Fit Testing
Karlsruhe Institute of Technology
Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD
Jul 27, 2026Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán SzabóMaximum Mean Discrepancy Minimax Estimation
Chair of Information Systems, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany · Department of Statistics, London School of Economics, Houghton Street, London, WC2A 2AE, UK
Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere
Jun 16, 2026Léo Nicollier, Enric Meinhardt-Llopis, Max Dunitz +3Maximum Mean Discrepancy Hyperspherical Representation Learning
CB, ATT · Université Paris-Saclay, CNRS, ENS Paris-Saclay, Centre Borelli, France · CB +4
Nyström Kernel Stein Discrepancy Tests
May 24, 2026Florian Kalinke, Zoltán Szabó, Bharath K. SriperumbudurGoodness-of-Fit Testing Nyström Method
Chair of Information Systems Karlsruhe Institute of Technology Am Fasanengarten 5, 76131 Karlsruhe, Germany · Department of Statistics London School of Economics Houghton Street, London, WC2A 2AE, UK · Department of Statistics The Pennsylvania State UniversityMay University Park, PA 16802, USA
Kernel Tests of Equivalence
Mar 11, 2026Xing Liu, Axel GandyKernel Stein Discrepancy Statistical Hypothesis Testing
QuantCo, London, UK · Department of Mathematics, Imperial College London, London, UK

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Task-Calibrated Random Features via Influence-Based Error Cancellation

Shinto Eguchi

Kernel Stein Discrepancy Random Feature Methods Kernel Ridge Regression

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Random features approximate kernel integrals by Monte Carlo, yet only selected frequency-sampling errors affect a downstream task. We propose Task-Calibrated Random Features (TCRF), which balance frequency moments constructed from task-specific influence using weights shared across kernel entries. Closed-form weights coincide with classical minimum-distance calibration; the contribution is a reusable, task-derived calibration space. We show that first-order variance equals the residual variance after projection onto this space. For kernel ridge regression, complete cancellation yields a non-Gaussian second-order limit. For kernel Stein discrepancy, a subset identity and disjoint-block variance decomposition eliminate the linear component of pilot noise. Positive exponential-tilting weights preserve the positive-semidefinite feature representation and support higher feasible calibration ranks. Paired experiments show improved approximation to exact kernel ridge regression at larger frequency budgets and to the exact empirical Stein discrepancy under a fixed alternative, even after accounting for a frequency-equivalent pilot budget. Gains are smaller with cross-validated ridge, landmark pilots require conservative rank selection, and near the Stein null allocating the pilot budget to additional ordinary frequencies is preferable.