364 citations · 1.8k across the 27 of their papers we have counts for
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Learning Hierarchical and Topographic Dictionaries with Structured Sparsity
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski +1
Recent work in signal processing and statistics have focused on defining new regularization functions, which not only induce sparsity of the solution, but also take into account th…
Group Lasso with Overlaps: the Latent Group Lasso approach
Guillaume Obozinski, Laurent Jacob, Jean-Philippe Vert
We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variables. We call the obtained formula…
Trace Lasso: a trace norm regularization for correlated designs
Edouard Grave, Guillaume Obozinski, Francis Bach
Using the -norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper,…
Structured sparsity through convex optimization
Francis Bach, Rodolphe Jenatton, Julien Mairal +1
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or…
Optimization with Sparsity-Inducing Penalties
Francis Bach, Rodolphe Jenatton, Julien Mairal +1
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extens…
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel +4
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning.…
Convex and Network Flow Optimization for Structured Sparsity
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski +1
We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of l_2- or l_infinity-norms over groups of variables. Whereas much ef…