Model Consistency for Learning with Mirror-Stratifiable Regularizers

Jalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré

Abstract: 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 learning. Model cons...