40 citations · 87 across the 11 of their papers we have counts for
12 papers
Gaussian-Smoothed Sliced Probability Divergences
Mokhtar Z. Alaya, Alain Rakotomamonjy, Maxime Berar +1
Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown that it…
Stochastic gradient descent with gradient estimator for categorical features
Paul Peseux, Maxime Berar, Thierry Paquet +1
Categorical data are present in key areas such as health or supply chain, and this data require specific treatment. In order to apply recent machine learning models on such data, e…
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences
Alain Rakotomamonjy, Mokhtar Z. Alaya, Maxime Berar +1
Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown, in appl…
Heterogeneous Wasserstein Discrepancy for Incomparable Distributions
Mokhtar Z. Alaya, Gilles Gasso, Maxime Berar +1
Optimal Transport (OT) metrics allow for defining discrepancies between two probability measures. Wasserstein distance is for longer the celebrated OT-distance frequently-used in t…
Optimal Transport for Conditional Domain Matching and Label Shift
Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso +3
We address the problem of unsupervised domain adaptation under the setting of generalized target shift (joint class-conditional and label shifts). For this framework, we theoretica…
Screening Sinkhorn Algorithm for Regularized Optimal Transport
Mokhtar Z. Alaya, Maxime Bérar, Gilles Gasso +1
We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between two discrete measures. After identifying neglectable components of the dual…