Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

When dealing with electro or magnetoencephalography records, many supervised
prediction tasks are solved by working with covariance matrices to summarize the signals …

Optimal transport is a long‐standing theory that has been studied in depth from both
theoretical and numerical point of views. Starting from the 50s this theory has also found a …

Many variants of the Wasserstein distance have been introduced to reduce its original
computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages …

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 …

Inspired by recent experiments that highlight the role of nematic defects in the
morphogenesis of epithelial tissues, we develop a minimal framework to study the dynamics …

Sliced optimal transport, which is basically a Radon transform followed by one-dimensional
optimal transport, became popular in various applications due to its efficient computation. In …

Sliced optimal transport reduces optimal transport on multi-dimensional domains to transport
on the line. More precisely, sliced optimal transport is the concatenation of the well-known …

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 …

Comparing spherical probability distributions is of great interest in various fields, including
geology, medical domains, computer vision, and deep representation learning. The utility of …