Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

Transport-based techniques for signal and data analysis have recently received increased
interest. Given their ability to provide accurate generative models for signal intensities and …

The Wasserstein distance and its variations, eg, the sliced-Wasserstein (SW) distance, have
recently drawn attention from the machine learning community. The SW distance …

Domain adaptation is one of the most challenging tasks of modern data analytics. If the
adaptation is done correctly, models built on a specific data representation become more …

This paper details a general numerical framework to approximate solutions to linear
programs related to optimal transport. The general idea is to introduce an entropic …

This paper introduces a new class of algorithms for optimization problems involving optimal
transportation over geometric domains. Our main contribution is to show that optimal …

We analyze two algorithms for approximating the general optimal transport (OT) distance
between two discrete distributions of size $ n $, up to accuracy $\varepsilon $. For the first …

This paper deals with the problem of quantifying the impact of model misspecification when
computing general expected values of interest. The methodology that we propose is …

Learning to predict multi-label outputs is challenging, but in many problems there is a
natural metric on the outputs that can be used to improve predictions. In this paper we …