4 papers · 1 filter
Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
Ferdinand Genans, Erwan Scornet
Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce syste…
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…
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…
Semi-Discrete Optimal Transport: Nearly Minimax Estimation With Stochastic Gradient Descent and Adaptive Entropic Regularization
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
Optimal Transport (OT) based distances are powerful tools for machine learning to compare probability measures and manipulate them using OT maps. In this field, a setting of intere…