3 papers
stat.ML2026
On the Wasserstein Geodesic Principal Component Analysis of probability measures
Nina Vesseron, Elsa Cazelles, Alice Le Brigant +1
This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geometry. The goal is to identify geodesi…
stat.ML2025
Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
Nina Vesseron, Louis Béthune, Marco Cuturi
The canonical approach in generative modeling is to split model fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it (e.g. u…
stat.ML2025
On a Neural Implementation of Brenier's Polar Factorization
Nina Vesseron, Marco Cuturi
In 1991, Brenier proved a theorem that generalizes the polar decomposition for square matrices -- factored as PSD unitary -- to any vector field $F:\mathbb{R}^d\rightarrow…