A smoothing approach for composite conditional gradient with nonsmooth loss

Federico Pierucci 1 Zaid Harchaoui 1 Jérôme Malick 2
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 BIPOP - Modelling, Simulation, Control and Optimization of Non-Smooth Dynamical Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We consider learning problems where the non-smoothness lies both in the convex empirical risk and in the regularization penalty. Examples of such problems include learning with nonsmooth loss functions and atomic decomposition regularization penalty. Such doubly nonsmooth learning problems prevent the use of recently proposed composite conditional gradient algorithms for training, which are particularly attractive for large-scale applications. Indeed, they rely on the assumption that the empirical risk part of the objective is smooth. We propose a composite conditional gradient algorithm with smoothing to tackle such learning problems. We set up a framework allowing to systematically design parametrized smooth surrogates of nonsmooth loss functions. We then propose a smoothed composite conditional gradient algorithm, for which we prove theoretical guarantees on the accuracy. We present promising experimental results on collaborative filtering tasks.
Liste complète des métadonnées


https://hal.inria.fr/hal-01096630
Contributeur : Thoth Team <>
Soumis le : jeudi 15 janvier 2015 - 12:30:45
Dernière modification le : vendredi 16 septembre 2016 - 15:15:23
Document(s) archivé(s) le : jeudi 10 septembre 2015 - 23:41:05

Fichier

RR-8662.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01096630, version 1

Citation

Federico Pierucci, Zaid Harchaoui, Jérôme Malick. A smoothing approach for composite conditional gradient with nonsmooth loss. [Research Report] RR-8662, INRIA Grenoble. 2014. <hal-01096630>

Partager

Métriques

Consultations de
la notice

686

Téléchargements du document

367