Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels
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Abstract (arXiv)
The aim of this paper is twofold. Based on the geometric Wasserstein tangent space, we first introduce Wasserstein steepest descent flows. These are locally absolutely continuous curves in the Wasserstein space whose tangent vectors point into a steepest descent direction of a given functional. This allows the use of Euler forward schemes instead of Jordan--Kinderlehrer--Otto schemes. For λ-convex functionals, we show that Wasserstein steepest descent flows are an equivalent characterization of Wasserstein gradient flows. The second aim is to study Wasserstein flows of the maximum mean discrepancy with respect to certain Riesz kernels. The crucial part is hereby the treatment of the interaction energy. Although it is not λ-convex along generalized geodesics, we give analytic expressions for Wasserstein steepest descent flows of the interaction energy starting at Dirac measures. In contrast to smooth kernels, the particle may explode, i.e., a Dirac measure becomes a non-Dirac one. The computation of steepest descent flows amounts to finding equilibrium measures with external fields, which nicely links Wasserstein flows of interaction energies with potential theory. Finally, we provide numerical simulations of Wasserstein steepest descent flows of discrepancies.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2022.
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Citing papers per year: 2023: 3; 2024: 5; 2025: 4; 2026: 4.
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- 2023Generative Sliced MMD Flows with Riesz Kernelscited by 36
- 2024Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matchingcited by 34
- 2023Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernelcited by 25
- 2024Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spacescited by 15
- 2023Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernelscited by 9
- 2024Fast Kernel Summation in High Dimensions via Slicing and Fourier Transformscited by 7
- 2024Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functionscited by 7
- 2025Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regimecited by 4
- 2024Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergencecited by 3
- 2026Sobolev Regularized MMD Gradient Flowcited by 3
- 2025Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flowscited by 2
- 2025Stationary MMD Pointscited by 2
4 more
- 2026Wasserstein gradient flows for Coulomb discrepanciescited by 1
- 2025Adapting Noise to Data: Generative Flows from 1D Processescited by 0
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimizationcited by 0
- 2026A Gradient Flow Perspective on Minimum MMD Estimationcited by 0
- 2026Wasserstein gradient flows for Coulomb discrepancies14 Jul 2026
- 2026A Gradient Flow Perspective on Minimum MMD Estimation4 Jul 2026
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization26 Jun 2026
- 2026Sobolev Regularized MMD Gradient Flow12 May 2026
- 2025Adapting Noise to Data: Generative Flows from 1D Processes14 Oct 2025
- 2025Stationary MMD Points27 May 2025
- 2025Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime25 Apr 2025
- 2025Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows10 Apr 2025
- 2024Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence29 Aug 2024
- 2024Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functions14 Aug 2024
- 2024Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching27 Mar 2024
- 2024Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces7 Feb 2024
4 more
- 2024Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms16 Jan 2024
- 2023Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel4 Oct 2023
- 2023Generative Sliced MMD Flows with Riesz Kernels19 May 2023
- 2023Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels27 Jan 2023
- 2023Generative Sliced MMD Flows with Riesz Kernelscited by 36
- 2023Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernelcited by 25
- 2024Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spacescited by 15
- 2023Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernelscited by 9
- 2024Fast Kernel Summation in High Dimensions via Slicing and Fourier Transformscited by 7
- 2024Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functionscited by 7
- 2025Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regimecited by 4
- 2024Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergencecited by 3
- 2026Sobolev Regularized MMD Gradient Flowcited by 3
- 2025Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flowscited by 2
- 2025Stationary MMD Pointscited by 2
- 2026Wasserstein gradient flows for Coulomb discrepanciescited by 1
12 most cited of 15 All 15 (JSON)
- 2026Wasserstein gradient flows for Coulomb discrepancies14 Jul 2026
- 2026A Gradient Flow Perspective on Minimum MMD Estimation4 Jul 2026
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization26 Jun 2026
- 2026Sobolev Regularized MMD Gradient Flow12 May 2026
- 2025Adapting Noise to Data: Generative Flows from 1D Processes14 Oct 2025
- 2025Stationary MMD Points27 May 2025
- 2025Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime25 Apr 2025
- 2025Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows10 Apr 2025
- 2024Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence29 Aug 2024
- 2024Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functions14 Aug 2024
- 2024Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces7 Feb 2024
- 2024Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms16 Jan 2024
12 newest of 15 All 15 (JSON)