Sampleability transport, nonlinear regularization, and the porous medium flow
Hy P.G. Lam

TL;DR
This paper explores the Wasserstein projection for probability measures with bounded density ratios, analyzing porous medium flow and nonlinear diffusion, and establishing foundational properties and obstructions within this framework.
Contribution
It proves existence and uniqueness of the sampleability projection, analyzes porous medium equations as regularizers, and identifies structural obstructions in the nonlinear diffusion approach.
Findings
Proved existence and uniqueness of the sampleability projection.
Analyzed porous medium equation properties like finite support propagation.
Identified a structural obstruction in porous-medium sampleability theory.
Abstract
We study the Wasserstein projection of a compactly supported probability measure onto the class of measures whose density ratio is bounded, and we place this projection in a broader program connecting generative modeling, optimal transport, and nonlinear diffusion. The paper proves existence and uniqueness of the sampleability projection, uniqueness of the Brenier map at the minimizer, path independence of the quadratic Wasserstein generation loss, and the diffusion-threshold picture for the heat semigroup. The porous medium equation is then analyzed as a candidate forward regularizer. We prove the two rigorous properties that make the equation attractive for this purpose, namely finite propagation of compact support and an explicit Wasserstein cost bound obtained from dissipation of the R\'enyi entropy. We then identify a structural obstruction inherent to any porous-medium version…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
