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 …

We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a
generative model of the data distribution. WAE minimizes a penalized form of the …

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 …

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In
this new model, we show that we can improve the stability of learning, get rid of problems …

In computer vision, one is often confronted with problems of domain shifts, which occur when
one applies a classifier trained on a source dataset to target data sharing similar …

The ability to compare two degenerate probability distributions, that is two distributions
supported on low-dimensional manifolds in much higher-dimensional spaces, is a crucial …

Computing optimal transport distances such as the earth mover's distance is a fundamental
problem in machine learning, statistics, and computer vision. Despite the recent introduction …

There is intense interest in applying machine learning to problems of causal inference in
fields such as healthcare, economics and education. In particular, individual-level causal …

Unsupervised domain adaptation (UDA) is a representative problem in transfer learning,
which aims to improve the classification performance on an unlabeled target domain by …

Optimal transport (OT) and maximum mean discrepancies (MMD) are now routinely used in
machine learning to compare probability measures. We focus in this paper on Sinkhorn …