3 papers
stat.ML2026
Geometry-Aware Optimal Transport: Fast Intrinsic Dimension and Wasserstein Distance Estimation
Ferdinand Genans, Olivier Wintenberger
Solving large scale Optimal Transport (OT) in machine learning typically relies on sampling measures to obtain a tractable discrete problem. While the discrete solver's accuracy is…
stat.ML2025
Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
Adding entropic regularization to Optimal Transport (OT) problems has become a standard approach for designing efficient and scalable solvers. However, regularization introduces a…
math.ST2025
Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
We investigate the semi-discrete Optimal Transport (OT) problem, where a continuous source measure is transported to a discrete target measure , with particular attention to…