3 citations · 5 across the 33 of their papers we have counts for
6 papers · 1 filter
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…
Multivariate Conformal Prediction using Optimal Transport
Michal Klein, Louis Bethune, Eugene Ndiaye +1
Conformal prediction (CP) quantifies the uncertainty of machine learning models by constructing sets of plausible outputs. These sets are constructed by leveraging a so-called conf…
Progressive Entropic Optimal Transport Solvers
Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein +3
Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds…
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However,…
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…
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Dominik Klein, Théo Uscidda, Fabian Theis +1
Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing…