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stat.ML2025
Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows
Nicolaj Rux, Michael Quellmalz, Gabriele Steidl
Negative distance kernels were used in the definition of maximum mean discrepancies (MMDs) in statistics and lead to favorable numerical results in various ap…
stat.ML2024★ 1 cited
Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces
Viktor Stein, Sebastian Neumayer, Nicolaj Rux +1
Commonly used -divergences of measures, e.g., the Kullback-Leibler divergence, are subject to limitations regarding the support of the involved measures. A remedy is regularizin…