Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernels
Matthew Rosenzweig, Dejan Slepčev, Lihan Wang
math.AP, math.PR, stat.ML
Submitted: 2026-08-02
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- On the global convergence of Wasserstein gradient flow of the Coulomb discrepancy
- (De)-regularized Maximum Mean Discrepancy Gradient Flow
- Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies
- Absolute moments and Fourier-based probability metrics
- Wasserstein gradient flows for Coulomb discrepancies
- Sharp Rates of MMD Empirical Estimation with Power Kernels
- Riesz energy problems with external fields and related theory
- Ten equivalent definitions of the fractional Laplace operator
- On Kato-Ponce and fractional Leibniz
- Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
- Lectures on Coulomb and Riesz gases
- Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
- On the role of the point at infinity in Deny's principle of positivity of mass for Riesz potentials
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