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Wasserstein Diffusion on Multidimensional Spaces

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arxiv 2401.12721 v3 pith:GQFVSTWF submitted 2024-01-23 math.PR math.FAmath.MG

classification math.PRmath.FAmath.MG
keywords betamathbbmathcaldiffusiongivenreversiblewassersteinassociated
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abstract

Given any closed Riemannian manifold $M$, we construct a reversible diffusion process on the space ${\mathcal P}(M)$ of probability measures on $M$ that is (i) reversible w.r.t.~the entropic measure ${\mathbb P}^\beta$ on ${\mathcal P}(M)$, heuristically given as $$d\mathbb{P}^\beta(\mu)=\frac{1}{Z} e^{-\beta \, \text{Ent}(\mu| m)}\ d\mathbb{P}^*(\mu);$$ (ii) associated with a regular Dirichlet form with carr\'e du champ derived from the Wasserstein gradient in the sense of Otto calculus $${\mathcal E}_W(f)=\liminf_{g\to f}\ \frac12\int_{{\mathcal P}(M)} \big\|\nabla_W g\big\|^2(\mu)\ d{\mathbb P}^\beta(\mu);$$ (iii) non-degenerate, at least in the case of the $n$-sphere and the $n$-torus.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Flowing Datasets with Wasserstein over Wasserstein Gradient Flows

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A new gradient flow framework on the space of probability distributions over probability distributions is introduced and applied to flowing labeled datasets between domains.

  2. Diffusion enabled Optimal Transport distances for graph matching

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Diffusing node features before semi-relaxed fused Gromov–Wasserstein matching improves synthetic graph alignment accuracy and ARI over plain srFGW, most under medium noise.

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