mlsanityData to 7 Oct 2026

A Unifying and Canonical Description of Measure-Preserving Diffusions

arXiv 2105.02845 ↗First posted 6 May 2021math.PR math.DG stat.MLcited by 4

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Abstract (arXiv)

A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework. In this paper, we develop a geometric theory that improves and generalises this construction to any manifold. We thereby demonstrate that the completeness result is a direct consequence of the topology of the underlying manifold and the geometry induced by the target measure P; there is no need to introduce other structures such as a Riemannian metric, local coordinates, or a reference measure. Instead, our framework relies on the intrinsic geometry of P and in particular its canonical derivative, the deRham rotationnel, which allows us to parametrise the Fokker--Planck currents of measure-preserving diffusions using potentials. The geometric formalism can easily incorporate constraints and symmetries, and deliver new important insights, for example, a new complete recipe of Langevin-like diffusions that are suited to the construction of samplers. We also analyse the reversibility and dissipative properties of the diffusions, the associated deterministic flow on the space of measures, and the geometry of Langevin processes. Our article connects ideas from various literature and frames the theory of measure-preserving diffusions in its appropriate mathematical context.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 166 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

30 of 166 references.

References · 30

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18 more
  1. 2018Global Non-convex Optimization with Discretized Diffusionscited by 44
  2. 2018Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Accelerationcited by 31
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  18. 2020Constraint-Based Regularization of Neural Networkscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

4 papers.