353 citations · 1.8k across the 69 of their papers we have counts for
9 papers · 1 filter
A theory of optimal convex regularization for low-dimensional recovery
Yann Traonmilin, Rémi Gribonval, Samuel Vaiter
We consider the problem of recovering elements of a low-dimensional model from under-determined linear measurements. To perform recovery, we consider the minimization of a convex r…
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
Titouan Vayer, Rémi Gribonval
Comparing probability distributions is at the crux of many machine learning algorithms. Maximum Mean Discrepancies (MMD) and Wasserstein distances are two classes of distances betw…
Spurious Valleys, NP-hardness, and Tractability of Sparse Matrix Factorization With Fixed Support
Quoc-Tung Le, Elisa Riccietti, Rémi Gribonval
The problem of approximating a dense matrix by a product of sparse factors is a fundamental problem for many signal processing and machine learning tasks. It can be decomposed into…
Identifiability in Two-Layer Sparse Matrix Factorization
Léon Zheng, Elisa Riccietti, Rémi Gribonval
Sparse matrix factorization is the problem of approximating a matrix by a product of sparse factors .…
Efficient Identification of Butterfly Sparse Matrix Factorizations
Léon Zheng, Elisa Riccietti, Rémi Gribonval
Fast transforms correspond to factorizations of the form , where each factor is sparse and possibly st…
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
Barbara Pascal, Patrice Abry, Nelly Pustelnik +3
Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermea…
An Embedding of ReLU Networks and an Analysis of their Identifiability
Pierre Stock, Rémi Gribonval
Neural networks with the Rectified Linear Unit (ReLU) nonlinearity are described by a vector of parameters , and realized as a piecewise linear continuous function $R_θ: x \in \…
Fast Multiscale Diffusion on Graphs
Sibylle Marcotte, Amélie Barbe, Rémi Gribonval +4
Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix. We tighten a bound on the approximation e…
Minibatch optimal transport distances; analysis and applications
Kilian Fatras, Younes Zine, Szymon Majewski +3
Optimal transport distances have become a classic tool to compare probability distributions and have found many applications in machine learning. Yet, despite recent algorithmic de…