385 citations · 1.1k across the 45 of their papers we have counts for
4 papers · 1 filter
Data Dependent Kernel Approximation using Pseudo Random Fourier Features
Bharath Bhushan Damodaran, Nicolas Courty, Philippe-Henri Gosselin
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to the pairwise evaluations in kernel methods, the complexity of kernel computatio…
Large-Scale Optimal Transport and Mapping Estimation
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary +3
This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) pla…
Learning Wasserstein Embeddings
Nicolas Courty, Rémi Flamary, Mélanie Ducoffe
The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numer…
Joint Distribution Optimal Transportation for Domain Adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard +1
This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function in a given target domain without any labeled sample by explo…