We study the sample complexity of entropic optimal transport in high dimensions using
computationally efficient plug-in estimators. We significantly advance the state of the art by …

Entropic Brenier maps are regularized analogues of Brenier maps (optimal transport maps)
which converge to Brenier maps as the regularization parameter shrinks. In this work, we …

We investigate the optimal transport problem between probability measures when the
underlying cost function is understood to satisfy a least action principle, also known as a …

As the problem of minimizing functionals on the Wasserstein space encompasses many
applications in machine learning, different optimization algorithms on $\mathbb {R}^ d …

Conditional simulation is a fundamental task in statistical modeling: Generate samples from
the conditionals given finitely many data points from a joint distribution. One promising …

Matching a source to a target probability measure is often solved by instantiating a linear
optimal transport (OT) problem, parameterized by a ground cost function that quantifies …

A transport map is versatile and useful for many downstream tasks, from training generative
modeling to solving Bayesian inference problems.\cite {marzouk2016introduction} …