9 citations · 11 across the 2 of their papers we have counts for
2 papers
cs.LG2017★ 2 cited
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…
stat.ML2017★ 9 cited
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…