Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but
they have thus far been held back by limitations in their simulation-based maximum …

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …

The dynamic Schrödinger bridge problem seeks a stochastic process that defines a
transport between two target probability measures, while optimally satisfying the criteria of …

This is a comprehensive review of the strong-interaction limit of density functional theory. It
covers the derivation of the limiting strictly correlated electrons (SCE) functional from exact …

We present simulation-free score and flow matching ([SF] $^ 2$ M), a simulation-free
objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary …

Statistical Optimal Transport arxiv:2407.18163v2 [math.ST] 7 Nov 2024 Page 1 Statistical
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …

We prove a central limit theorem for the entropic transportation cost between subgaussian
probability measures, centered at the population cost. This is the first result which allows for …

Although optimal transport (OT) problems admit closed form solutions in a very few notable
cases, eg in 1D or between Gaussians, these closed forms have proved extremely fecund …

The static optimal transport $(\mathrm {OT}) $ problem between Gaussians seeks to recover
an optimal map, or more generally a coupling, to morph a Gaussian into another. It has been …

We study first-order optimization algorithms for computing the barycenter of Gaussian
distributions with respect to the optimal transport metric. Although the objective is …