High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

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 …

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 …

Optimal transport theory has provided machine learning with several tools to infer a push-
forward map between densities from samples. While this theory has recently seen …

Monitoring and maintaining machine learning models are among the most critical
challenges in translating recent advances in the field into real-world applications. However …

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 …

We study stability of optimizers and convergence of Sinkhorn's algorithm in the framework of
entropic optimal transport. We show entropic stability for optimal plans in terms of the …

Tools from optimal transport (OT) theory have recently been used to define a notion of
quantile function for directional data. In practice, regularization is mandatory for applications …

Transportation of samples across different domains is a central task in several machine
learning problems. A sensible requirement for domain transfer tasks in computer vision and …

Within the field of optimal transport (OT), the choice of ground cost is crucial to ensuring that
the optimality of a transport map corresponds to usefulness in real-world applications. It is …