17 citations · 75 across the 23 of their papers we have counts for
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stat.ML2017★ 12 cited
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Mathurin Massias, Olivier Fercoq, Alexandre Gramfort +1
In high dimension, it is customary to consider Lasso-type estimators to enforce sparsity. For standard Lasso theory to hold, the regularization parameter should be proportional to…
stat.ML2017★ 8 cited
From safe screening rules to working sets for faster Lasso-type solvers
Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Convex sparsity-promoting regularizations are ubiquitous in modern statistical learning. By construction, they yield solutions with few non-zero coefficients, which correspond to s…