Publication

Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies

2026

Abstract

We study the quantitative convergence of Wasserstein gradient flows of Kernel Mean Discrepancy (KMD) (also known as Maximum Mean Discrepancy (MMD)) functionals. Our setting covers in particular the training dynamics of shallow neural networks in the infinite-width and continuous time limit, as well as interacting particle systems with pairwise Riesz kernel interaction in the mean-field and overdamped limit.

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2026

Abstract

Official source

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