5 citations · 7 across the 2 of their papers we have counts for
4 papers
Sparse Multiple Kernel Learning: Support Identification via Mirror Stratifiability
Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa
In statistical machine learning, kernel methods allow to consider infinite dimensional feature spaces with a computational cost that only depends on the number of observations. Thi…
Model Consistency for Learning with Mirror-Stratifiable Regularizers
Jalal Fadili, Guillaume Garrigos, Jérome Malick +1
Low-complexity non-smooth convex regularizers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised lea…
Thresholding gradient methods in Hilbert spaces: support identification and linear convergence
Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa
We study regularized least squares optimization problem in a separable Hilbert space. We show that the iterative soft-thresholding algorithm (ISTA) converges linearly, wit…
Multiibjective optimization : an inertial dynamical approach to Pareto optima
Hédy Attouch, Guillaume Garrigos
We present some first results concerning a gradient-based dynamic approach to multi-objective optimization problems, involving inertial effects. We prove the existence of global so…