Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs)
have become the preferred option for generative modeling, with numerous pre-trained …

We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a
novel and theoretically-supported paradigm for large-scale graph analysis. The proposed …

Establishing dense correspondences across semantically similar images is a challenging
task. Due to the large intra-class variation and background clutter, two common issues occur …

Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-
SW), have been used widely in the recent years due to their fast computation and scalability …

The ability to align points across two related yet incomparable point clouds (eg living in
different spaces) plays an important role in machine learning. The Gromov-Wasserstein …

Comparing metric measure spaces (ie a metric space endowed with a probability
distribution) is at the heart of many machine learning problems. The most popular distance …

Optimal transport theory has recently found many applications in machine learning thanks to
its capacity to meaningfully compare various machine learning objects that are viewed as …

Understanding the phase stability of a chemical system constitutes the foundation of
materials science. Knowledge of the equilibrium state of a system under arbitrary …

Optimal transport distances are powerful tools to compare probability distributions and have
found many applications in machine learning. Yet their algorithmic complexity prevents their …