Optimal transport has recently been reintroduced to the machine learning community thanks
in part to novel efficient optimization procedures allowing for medium to large scale …

Optimal Transport (OT) theory has seen an increasing amount of attention from the computer
science community due to its potency and relevance in modeling and machine learning. It …

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 …

Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

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 …

Several recent applications of optimal transport (OT) theory to machine learning have relied
on regularization, notably entropy and the Sinkhorn algorithm. Because matrix-vector …

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 …

Optimal transport distances have become a classic tool to compare probability distributions
and have found many applications in machine learning. Yet, despite recent algorithmic …

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 …

Optimal transport (OT) defines a powerful framework to compare probability distributions in a
geometrically faithful way. However, the practical impact of OT is still limited because of its …