17 citations · 75 across the 23 of their papers we have counts for
26 papers
Principled MAP estimation for inverse problems: bridging the gap between convergence and performance
Alexandre Lagier, Valentine Tosel, Anne Gagneux +2
Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-noise-level denoisers within fir…
Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching
Ségolène Martin, Anne Gagneux, Quentin Bertrand +2
Flow matching and diffusion can be trained to predict different quantities, most commonly the data , the source noise , or the velocity~. Although theoretically equiva…
Training Flow Matching: The Role of Weighting and Parameterization
Anne Gagneux, Ségolène Martin, Rémi Gribonval +1
We study the training objectives of denoising-based generative models, with a particular focus on loss weighting and output parameterization, including noise-, clean image-, and ve…
Efficient Solvers for SLOPE in R, Python, Julia, and C++
Johan Larsson, Malgorzata Bogdan, Krystyna Grzesiak +2
We present a suite of packages in R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. The packages feature a highly efficient hy…
The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, Ségolène Martin, Rémi Gribonval +1
Flow matching has achieved remarkable success, yet the factors influencing the quality of its generation process remain poorly understood. In this work, we adopt a denoising perspe…
Proximal Operators of Sorted Nonconvex Penalties
Anne Gagneux, Mathurin Massias, Emmanuel Soubies
This work studies the problem of sparse signal recovery with automatic grouping of variables. To this end, we investigate sorted nonsmooth penalties as a regularization approach fo…
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Julián Tachella, Matthieu Terris, Samuel Hurault +24
DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementa…
On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity
Quentin Bertrand, Anne Gagneux, Mathurin Massias +1
Modern deep generative models can now produce high-quality synthetic samples that are often indistinguishable from real training data. A growing body of research aims to understand…
Convexity in ReLU Neural Networks: beyond ICNNs?
Anne Gagneux, Mathurin Massias, Emmanuel Soubies +1
Convex functions and their gradients play a critical role in mathematical imaging, from proximal optimization to Optimal Transport. The successes of deep learning has led many to u…
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure
Can Pouliquen, Mathurin Massias, Titouan Vayer
Estimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various con…
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso
Can Pouliquen, Paulo Gonçalves, Mathurin Massias +1
We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method. In particular, we de…
Coordinate Descent for SLOPE
Johan Larsson, Quentin Klopfenstein, Mathurin Massias +1
The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved.…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…
Beyond L1: Faster and Better Sparse Models with skglm
Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier +2
We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions…
Iterative regularization for low complexity regularizers
Cesare Molinari, Mathurin Massias, Lorenzo Rosasco +1
Iterative regularization exploits the implicit bias of an optimization algorithm to regularize ill-posed problems. Constructing algorithms with such built-in regularization mechani…
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias +4
Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order method…
Anderson acceleration of coordinate descent
Quentin Bertrand, Mathurin Massias
Acceleration of first order methods is mainly obtained via inertial techniques à la Nesterov, or via nonlinear extrapolation. The latter has known a recent surge of interest, with…
Iterative regularization for convex regularizers
Cesare Molinari, Mathurin Massias, Lorenzo Rosasco +1
We study iterative regularization for linear models, when the bias is convex but not necessarily strongly convex. We characterize the stability properties of a primal-dual gradient…
Dimension-free convergence rates for gradient Langevin dynamics in RKHS
Boris Muzellec, Kanji Sato, Mathurin Massias +1
Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracted considerable attention lately, as a way to provide convergence guarantees in a non-convex setting. However…
Support recovery and sup-norm convergence rates for sparse pivotal estimation
Mathurin Massias, Quentin Bertrand, Alexandre Gramfort +1
In high dimensional sparse regression, pivotal estimators are estimators for which the optimal regularization parameter is independent of the noise level. The canonical pivotal est…
Dual Extrapolation for Sparse Generalized Linear Models
Mathurin Massias, Samuel Vaiter, Alexandre Gramfort +1
Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables. For statis…
Learning step sizes for unfolded sparse coding
Pierre Ablin, Thomas Moreau, Mathurin Massias +1
Sparse coding is typically solved by iterative optimization techniques, such as the Iterative Shrinkage-Thresholding Algorithm (ISTA). Unfolding and learning weights of ISTA using…
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Quentin Bertrand, Mathurin Massias, Alexandre Gramfort +1
Sparsity promoting norms are frequently used in high dimensional regression. A limitation of such Lasso-type estimators is that the optimal regularization parameter depends on the…
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, st…