R. J. Adler, An Introduction to Continuity, Extrema, and Related Topics for General Gaussian Processes, IMS, 1990.

A. Argyriou, T. Evgeniou, and M. Pontil, Convex multi-task feature learning, Machine Learning, pp.243-272, 2008.

F. Bach, Bolasso, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008.
DOI : 10.1145/1390156.1390161

URL : https://hal.archives-ouvertes.fr/hal-00271289

F. Bach, Consistency of the group Lasso and multiple kernel learning, Journal of Machine Learning Research, vol.9, pp.1179-1225, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00164735

F. Bach, Self-concordant analysis for logistic regression, Electronic Journal of Statistics, vol.4, issue.0, 2009.
DOI : 10.1214/09-EJS521

URL : https://hal.archives-ouvertes.fr/hal-00426227

F. Bach, High-dimensional non-linear variable selection through hierarchical kernel learning, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00413473

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, Model-Based Compressive Sensing, IEEE Transactions on Information Theory, vol.56, issue.4, 2008.
DOI : 10.1109/TIT.2010.2040894

P. Bickel, Y. Ritov, and A. Tsybakov, Simultaneous analysis of Lasso and Dantzig selector, The Annals of Statistics, vol.37, issue.4, pp.1705-1732, 2009.
DOI : 10.1214/08-AOS620

URL : https://hal.archives-ouvertes.fr/hal-00401585

J. M. Borwein and A. S. Lewis, Convex Analysis and Nonlinear Optimization: Theory and Examples, 2006.

S. P. Boyd and L. Vandenberghe, Convex Optimization, 2004.

P. J. Cameron, Combinatorics: Topics, Techniques, Algorithms, 1994.

F. R. Chung, Spectral Graph Theory, 1997.
DOI : 10.1090/cbms/092

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, European Conference on Computer Vision, 2006.
DOI : 10.1023/A:1008162616689

URL : https://hal.archives-ouvertes.fr/inria-00548587

J. P. Doignon and J. C. Falmagne, Knowledge Spaces, 1998.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of statistics, vol.32, issue.2, pp.407-451, 2004.

W. Fu and K. Knight, Asymptotics for lasso-type estimators, The Annals of Statistics, vol.28, issue.5, pp.1356-1378, 2000.
DOI : 10.1214/aos/1015957397

A. Gramfort and M. Kowalski, Improving M/EEG source localization with an inter-condition sparse prior, IEEE International Symposium on Biomedical Imaging, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00424029

H. Harzallah, F. Jurie, and C. Schmid, Combining efficient object localization and image classification, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459257

URL : https://hal.archives-ouvertes.fr/inria-00439516

L. He and L. Carin, Exploiting structure in wavelet-based Bayesian compressive sensing, IEEE Transactions on Signal Processing, vol.57, pp.3488-3497, 2009.

J. Huang and T. Zhang, The benefit of group sparsity, The Annals of Statistics, vol.38, issue.4, 2009.
DOI : 10.1214/09-AOS778

J. Huang, T. Zhang, and D. Metaxas, Learning with structured sparsity, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553429

L. Jacob, G. Obozinski, and J. Vert, Group lasso with overlap and graph lasso, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553431

R. Jenatton, G. Obozinski, and F. Bach, Structured sparse principal component analysis, International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
URL : https://hal.archives-ouvertes.fr/hal-00414158

S. Kim and E. P. Xing, Tree-guided group Lasso for multi-task regression with structured sparsity, 2009.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, Efficient sparse coding algorithms, Advances in Neural Information Processing Systems, 2007.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research, vol.11, issue.1, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

P. Massart, Concentration Inequalities and Model Selection: Ecole d'´ eté de Probabilités de Saint- Flour 23, 2003.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, 2008.
DOI : 10.1111/j.1467-9868.2010.00740.x

C. A. Micchelli and M. Pontil, Learning the kernel function via regularization, Journal of Machine Learning Research, vol.6, issue.2, p.1099, 2006.

Y. Nardi and A. Rinaldo, On the asymptotic properties of the group lasso estimator for linear models, Electronic Journal of Statistics, vol.2, issue.0, pp.605-633, 2008.
DOI : 10.1214/08-EJS200

G. Obozinski, B. Taskar, and M. I. Jordan, Joint covariate selection and joint subspace selection for multiple classification problems, Statistics and Computing, vol.8, issue.68, pp.1-22, 2009.
DOI : 10.1007/s11222-008-9111-x

M. R. Osborne, B. Presnell, and B. A. Turlach, On the Lasso and its dual, Journal of Computational and Graphical Statistics, vol.9, pp.319-337, 2000.

F. Rapaport, E. Barillot, and J. Vert, Classification of arrayCGH data using fused SVM, Bioinformatics, vol.24, issue.13, pp.375-382, 2008.
DOI : 10.1093/bioinformatics/btn188

URL : https://hal.archives-ouvertes.fr/inserm-00293893

R. T. Rockafellar, Convex Analysis, 1970.
DOI : 10.1515/9781400873173

S. Rosset, J. Zhu, and T. Hastie, Boosting as a regularized path to a maximum margin classifier, Journal of Machine Learning Research, vol.5, pp.941-973, 2004.

V. Roth and B. Fischer, The Group-Lasso for generalized linear models, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008.
DOI : 10.1145/1390156.1390263

P. Soille, Morphological Image Analysis: Principles and Applications, 2003.

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B, pp.267-288, 1996.

K. C. Toh, M. J. Todd, and R. H. Tütüncü, SDPT3?a MATLAB software package for semidefinite programming, version 1.3. Optimization Methods and Software, pp.545-581, 1999.

P. Tseng, Approximation accuracy, gradient methods, and error bound for structured convex optimization, Mathematical Programming, vol.68, issue.12, 2009.
DOI : 10.1007/s10107-010-0394-2

R. H. Tütüncü, K. C. Toh, and M. J. Todd, Solving semidefinite-quadratic-linear programs using SDPT3, Mathematical Programming, pp.189-217, 2003.

M. J. Wainwright, Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using <formula formulatype="inline"><tex Notation="TeX">$\ell _{1}$</tex> </formula>-Constrained Quadratic Programming (Lasso), IEEE Transactions on Information Theory, vol.55, issue.5, pp.2183-2202, 2009.
DOI : 10.1109/TIT.2009.2016018

Z. J. Xiang, Y. T. Xi, U. Hasson, and P. J. Ramadge, Boosting with spatial regularization, Advances in Neural Information Processing Systems, 2009.

G. X. Yuan, K. W. Chang, C. J. Hsieh, and C. J. Lin, A comparison of optimization methods for large-scale L1-regularized linear classification, 2009.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356

T. Zhang, Some sharp performance bounds for least squares regression with L 1 regularization, The Annals of Statistics, vol.37, issue.5A, pp.2109-2144, 2009.
DOI : 10.1214/08-AOS659

P. Zhao and B. Yu, On model selection consistency of Lasso, Journal of Machine Learning Research, vol.7, pp.2541-2563, 2006.

P. Zhao, G. Rocha, and B. Yu, The composite absolute penalties family for grouped and hierarchical variable selection, The Annals of Statistics, vol.37, issue.6A, pp.3468-3497, 2009.
DOI : 10.1214/07-AOS584

H. Zou, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006.
DOI : 10.1198/016214506000000735

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998