In this paper, we present a novel and principled approach to learn the optimal transport
between two distributions, from samples. Guided by the optimal transport theory, we learn …

Despite the recent popularity of neural network-based solvers for optimal transport (OT),
there is no standard quantitative way to evaluate their performance. In this paper, we …

We present FlipTest, a black-box technique for uncovering discrimination in classifiers.
FlipTest is motivated by the intuitive question: had an individual been of a different protected …

Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory
issue of OT in large-scale applications. Despite their practicality, m-OT suffers from …

Wasserstein Barycenter is a principled approach to represent the weighted mean of a given
set of probability distributions, utilizing the geometry induced by optimal transport. In this …

This paper focuses on computing the convex conjugate operation that arises when solving
Euclidean Wasserstein-2 optimal transport problems. This conjugation, which is also …

We propose a potential flow generator with optimal transport regularity, which can be easily
integrated into a wide range of generative models, including different versions of generative …

In this letter we extend recent developments in computational optimal transport to the setting
of Riemannian manifolds. In particular, we show how to learn optimal transport maps from …

In Lamb Waves based Structural Health Monitoring (LWSHM) of composite aeronautic
structures, Deep Learning (DL) methods have proven to be promising to monitor damage …

We present a new extension for Neural Optimal Transport (NOT) training procedure, capable
of accurately and efficiently estimating optimal transportation plan via specific regularisation …