Selected Publications


Talks


G. Gasso, Batch and online approaches for constrained classification: Neyman-Pearson and q-value classification, Seminar of GDR ISIS, Paris, 2011

G. Gasso, Regularization Frontier in Machine Learning, Tutorial of ECML PKDD, Antwerp, 2008

G. Gasso, DC approach for a family of non-convex problems in machine learning, Seminar of GDR ISIS Paris, 2014

Journal, Book Chapters


A. Rakotomamonjy, G.  Gasso Histogram of gradients of time-frequency representations for audio scene classification. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 23(1), 142-153, 2015.


A. Rakotomamonjy, R. Flamary, G. Gasso DC Proximal Newton for Nonconvex Optimization Problems. Neural Networks and Learning Systems, IEEE Transactions on , vol.PP, no.99, pp.1-1, 2015.


R. Flamary, A. Rakotomamonjy, G. Gasso Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning. Chapter in  Regularization, Optimization, Kernels, and Support Vector Machines, Edited by Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou, 2014


X. Tian, G. Gasso, S. Canu A Multiple kernel framework for inductive semi-supervised SVM learning. Neurocomputing, volume 90, pages 46-58, 2012 [Pdf]


A. Rakotomamonjy, R. Flamary, G. Gasso, S. Canu \ell_p − \ell_q penalty for Sparse Linear and Sparse Multiple Kernel Multi-Task Learning. IEEE Transactions On Neural Networks, 2011 [Pdf]


G. Gasso, A. Pappaioannou, M. Spivak, L. Bottou Batch and online learning algorithms for nonconvex neyman-pearson classification - ACM Transactions on Intelligent Systems and Technology (TIST)  Volume 2, Issue 3, April 201. [Pdf]

G Gasso, A Rakotomamonjy, S Canu  Recovering sparse signals with a certain family of non-convex penalties and DC programming - IEEE Transactions In Signal Processing, Vol 57, N°12, pp 4686-4698, 2009. [Pdf][Erratum][Code]

Karina Zapién, Gilles Gasso,  Thomas Gärtner,  Stéphane Canu,  Model Selection for Ranking SVM Using Regularization Path; Chapter in Machine Learning Book,  2009

Gilles Gasso and Karina Zapién and Stéphane Canu. Apprentissage semi supervisé via un SVM parcimonieux : calcul du chemin de régularisation. Revue I3 : Information - Interaction - Intelligence, Vol 8, N°2, pp 41-67, 2008 [Pdf]

Gilles Gasso, Karina Zapien and Stephane Canu, Smoothness and sparsity tuning for Semi-Supervised SVM, in Mining Massive Data Sets for Security - Advances in Data Mining, Search, Social Networks and Text Mining, and their Applications to Security Edited by Françoise Fogelman-Soulié, Domenico Perrotta, Jakub Piskorski, Ralf Steinberger, 2008

G. Gasso, G. Mourot, and J. Ragot, Prévision des maxima journaliers d'ozone - approche multimodèle, Journal Européen des Systèmes Automatisés, vol. 17, pp. 513-532, 2005

A. Rakotomamonjy, K. Gasso, S. Canu, P. Vannoorenberghe, Prévisions de concentrations d'ozone : comparaison de différentes méthodes statistiques de type boîte noire, Journal Européen de Systèmes Automatisés, Vol 39, pp 533-552, 2005.

G. Gasso, G. Mourot, and J. Ragot,  Identication de systèmes dynamiques non-linéaires : approche multi-modèle, In Commande floue 2 : de l'approximation à l'apprentissage, pp. 93-148, Hermes, 2003.


Conference Proceedings


F. Yger, M. Berar, G. Gasso, A. Rakotomamonjy, Adaptive Canonical Correlation Analysis based on Matrix Manifolds. Proc. of ICML 2012

F. Yger, M. Berar, G. Gasso, A. Rakotomamonjy, Oblique principal subspace tracking on manifold. Proc. of ICASSP 2012 [Pdf]

F. Yger, M. Berar, G. Gasso, A. Rakotomamonjy, A supervised strategy for deep kernel machine. Proc. of ESANN 2011

X. Tian, G. Gasso, S. Canu A Multi-kernel Framework for Inductive Semi-supervised Learning. Proc. of ESANN 2011 [Pdf]

X. Tian, R. Hérault, G. Gasso, S. Canu, Pré-apprentissage supervisé pour les réseaux profonds, Reconnaissance des Formes et Intelligence Artificielle (RFIA), 2010 [Pdf]

R. Flamary, A. Rakotomamonjy, G. Gasso, S. Canu , Sélection de variables pour l’apprentissage simultanée de tâches, in Conférence en Apprentissage (CAp’09), 2009.

R. Flamary, A. Rakotomamonjy, G. Gasso, and S. Canu, SVM Multi-Task Learning and Non convex Sparsity Measure, in The Learning Workshop, 2009.

G. Gasso, A. Rakotomamonjy, S. Canu, Solving non-convex Lasso type problems with DC programming, IEEE Workshop in Machine Learning and Signal processing (MLSP), Cancun, 2008 [Pdf]

G Mallet, G Gasso, S Canu , New methods for the identification of a stable subspace model for dynamical systems, IEEE Workshop on Machine Learning for Signal Processing, 2008 [Pdf]

Karina Zapien Arreola, Thomas Gärtner, Gilles Gasso, Stéphane Canu, Regularization path for Ranking SVM. Proc. of ESANN 415-420, 2008 [Pdf]

Gaëlle Loosli, Gilles Gasso, Stéphane Canu, Regularization paths for nu-SVM and nu-SVR, International Symposium on Neural Networks, 2007 [Pdf]

G. Gasso, K. Zapién, S. Canu, L1-Norm Regularization Path for Sparse Semi-Supervised Laplacian SVM, ICMLA 2007, Cincinnati, Ohio, USA.

K. Zapién, G. Gasso, S. Canu, Estimation of Tangent planes for Neighborhood Graph Correction, ESANN April 2007, Brugges. [Pdf]

G. Gasso, K. Zapién, S. Canu, Computing and Stopping the Solution Paths for µ-SVR, ESANN April 2007, Brugges. [Pdf]

Gilles Gasso, Karina Zapien, Stéphane Canu, Chemins de régularisation pour les modèles de régression de type nu-SVR, GRETSI 2007

S. Canu, V. Guigue, A. Rakotomamonjy, G. Gasso, Kernel LARS Algorithm, NIPS Workshop on accuracy-regularization frontier, 2005.

K. Pekpe, G. Mourot, G. Gasso, and J. Ragot, Identication of switching systems using change detection technique in the subspace framework, in Proc. of 43rd IEEE Conference on Decision and Control, 2004

K. Pekpe, G. Mourot, G. Gasso, and J. Ragot, Identication de systèmes à commutations par une technique de détection de ruptures de modèle dans le contexte des sous-espaces, in Proc. of CIFA, 2004

K. Pekpe, G. Gasso, G. Mourot, and J. Ragot, Subspace identication of switching model, in Proc. of 13th IFAC Symposium on System Identication
(SYSID'03), 2003