4 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…