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 …

Classification is a fundamental problem in machine learning, and considerable efforts have
been recently devoted to the demanding long-tailed setting due to its prevalence in nature …

Neural optimal transport techniques mostly use Euclidean cost functions, such as $\ell^ 1$
or $\ell^ 2$. These costs are suitable for translation tasks between related domains, but they …

Previous research on contrastive learning (CL) has primarily focused on pairwise views to
learn representations by attracting positive samples and repelling negative ones. In this …

The Sinkhorn operator has recently experienced a surge of popularity in computer vision
and related fields. One major reason is its ease of integration into deep learning frameworks …

Optimal transport (OT) is attracting increasing attention in machine learning. It aims to
transport a source distribution to a target one at minimal cost. In its vanilla form, the source …

Mean-field games arise in various fields, including economics, engineering, and machine
learning. They study strategic decision-making in large populations where the individuals …

Extracting physical parameters that cannot be directly measured from an observed data set
remains a great challenge in several fields of science and physics. In many of these …

Optimal Transport is a useful metric to compare probability distributions and to compute a
pairing given a ground cost. Its entropic regularization variant (eOT) is crucial to have fast …

Abstract Inverse Optimal Transport (IOT) studies the problem of inferring the underlying cost
that gives rise to an observation on coupling two probability measures. Couplings appear as …