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 …

Deep domain adaptation (DDA) approaches have recently been shown to perform better
than their shallow rivals with better modeling capacity on complex domains (eg, image …

Multi-source domain adaptation (DA) is more challenging than conventional DA because the
knowledge is transferred from several source domains to a target domain. To this end, we …

Optimal Transport (OT) problem investigates a transport map that bridges two distributions
while minimizing a given cost function. In this regard, OT between tractable prior distribution …

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 …

Using the principle of imitation learning and the theory of optimal transport we propose in
this paper a novel model for unsupervised domain adaptation named Teacher Imitation …

Monge map refers to the optimal transport map between two probability distributions and
provides a principled approach to transform one distribution to another. In spite of the rapid …

Energy-based models (EBMs) are known in the Machine Learning community for decades.
Since the seminal works devoted to EBMs dating back to the noughties, there have been a …

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 …