104 citations · 519 across the 26 of their papers we have counts for
29 papers
Smooth Bilevel Programming for Sparse Regularization
Clarice Poon, Gabriel Peyré
Iteratively reweighted least square (IRLS) is a popular approach to solve sparsity-enforcing regression problems in machine learning. State of the art approaches are more efficient…
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
Meyer Scetbon, Gabriel Peyré, Marco Cuturi
The ability to align points across two related yet incomparable point clouds (e.g. living in different spaces) plays an important role in machine learning. The Gromov-Wasserstein (…
Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Several recent applications of optimal transport (OT) theory to machine learning have relied on regularization, notably entropy and the Sinkhorn algorithm. Because matrix-vector pr…
Momentum Residual Neural Networks
Michael E. Sander, Pierre Ablin, Mathieu Blondel +1
The training of deep residual neural networks (ResNets) with backpropagation has a memory cost that increases linearly with respect to the depth of the network. A way to circumvent…
Fast and accurate optimization on the orthogonal manifold without retraction
Pierre Ablin, Gabriel Peyré
We consider the problem of minimizing a function over the manifold of orthogonal matrices. The majority of algorithms for this problem compute a direction in the tangent space, and…
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré
Comparing metric measure spaces (i.e. a metric space endowed with aprobability distribution) is at the heart of many machine learning problems. The most popular distance between su…