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.8, pp.1179-1225, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00164735

F. Bach, Exploring large feature spaces with hierarchical multiple kernel learning, Advances in Neural Information Processing Systems (NIPS), 2008.
URL : https://hal.archives-ouvertes.fr/hal-00319660

R. Baraniuk, Compressive sensing, 2008 42nd Annual Conference on Information Sciences and Systems, pp.118-121, 2007.
DOI : 10.1109/CISS.2008.4558479
URL : https://hal.archives-ouvertes.fr/hal-00452261

V. Bentkus, On the dependence of the Berry???Esseen bound on dimension, Journal of Statistical Planning and Inference, vol.113, issue.2, pp.385-402, 2003.
DOI : 10.1016/S0378-3758(02)00094-0

P. J. Bickel, Y. Ritov, and A. Tsybakov, Simultaneous analysis of Lasso and Dantzig selector, The Annals of Statistics, vol.37, issue.4, 2008.
DOI : 10.1214/08-AOS620
URL : https://hal.archives-ouvertes.fr/hal-00401585

J. F. Bonnans, J. C. Gilbert, C. Lemaréchal, and C. A. Sagastizbal, Numerical Optimization Theoretical and Practical Aspects, 2003.

S. Boucheron, G. Lugosi, and O. Bousquet, Concentration Inequalities, Advanced Lectures on Machine Learning, 2004.
DOI : 10.1007/978-1-4757-2440-0
URL : https://hal.archives-ouvertes.fr/hal-00777381

S. Boyd and L. Vandenberghe, Convex Optimization, 2003.

L. Breiman, Arcing classifier, Annals of Statistics, vol.26, issue.3, pp.801-849, 1998.

P. Bühlmann, Boosting for high-dimensional linear models, The Annals of Statistics, vol.34, issue.2, pp.559-583, 2006.
DOI : 10.1214/009053606000000092

F. Bunea, A. Tsybakov, and M. Wegkamp, Sparsity oracle inequalities for the Lasso, Electronic Journal of Statistics, vol.1, issue.0, pp.169-194, 2007.
DOI : 10.1214/07-EJS008
URL : https://hal.archives-ouvertes.fr/hal-00160646

E. Candès and M. Wakin, An Introduction To Compressive Sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.21-30, 2008.
DOI : 10.1109/MSP.2007.914731

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Review, vol.43, issue.1, pp.129-159, 2001.
DOI : 10.1137/S003614450037906X

A. Cohen, W. Dahmen, and R. Devore, Compressed sensing and best $k$-term approximation, Journal of the American Mathematical Society, vol.22, issue.1, 2006.
DOI : 10.1090/S0894-0347-08-00610-3

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of Statistics, vol.32, p.407, 2004.

B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap, 1998.
DOI : 10.1007/978-1-4899-4541-9

D. Freedman, Bootstrapping Regression Models, The Annals of Statistics, vol.9, issue.6, pp.1218-1228, 1981.
DOI : 10.1214/aos/1176345638

J. Friedman, T. H. , and R. Tibshirani, Pathwise coordinate optimization, The Annals of Applied Statistics, vol.1, issue.2, pp.302-332, 2007.
DOI : 10.1214/07-AOAS131

W. Fu, Penalized regressions: the bridge vs. the Lasso, Journal of Computational and Graphical Statistics, vol.7, issue.3, pp.397-416, 1998.

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

J. Fuchs, On Sparse Representations in Arbitrary Redundant Bases, IEEE Transactions on Information Theory, vol.50, issue.6, pp.1341-1344, 2004.
DOI : 10.1109/TIT.2004.828141

P. Garrigues and L. E. Ghaoui, An homotopy algorithm for the Lasso with online observations, Advances in Neural Information Processing Systems (NIPS) 21, 2009.

F. Götze, On the Rate of Convergence in the Multivariate CLT, The Annals of Probability, vol.19, issue.2, pp.724-739, 1991.
DOI : 10.1214/aop/1176990448

R. Horn and C. Johnson, Matrix Analysis, 1985.

J. Huang, S. Ma, and C. Zhang, Adaptive Lasso for sparse high-dimensional regression models, Statistica Sinica, vol.18, pp.1603-1618, 2008.

K. Lounici, Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators, Electronic Journal of Statistics, vol.2, issue.0, 2008.
DOI : 10.1214/08-EJS177
URL : https://hal.archives-ouvertes.fr/hal-00222251

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

H. M. Markowitz, The optimization of a quadratic function subject to linear constraints, Naval Research Logistics Quarterly, vol.3, issue.1-2, pp.111-133, 1956.
DOI : 10.1002/nav.3800030110

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

N. Meinshausen, Relaxed Lasso, Computational Statistics & Data Analysis, vol.52, issue.1, pp.374-393, 2007.
DOI : 10.1016/j.csda.2006.12.019

N. Meinshausen and P. Bühlmann, High-dimensional graphs and variable selection with the Lasso, The Annals of Statistics, vol.34, issue.3, pp.1436-1462, 2006.
DOI : 10.1214/009053606000000281

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

N. Meinshausen and B. Yu, Lasso-type recovery of sparse representations for high-dimensional data, The Annals of Statistics, vol.37, issue.1, pp.246-270, 2008.
DOI : 10.1214/07-AOS582

M. R. Osborne, B. Presnell, and B. A. Turlach, On the lasso and its dual, Journal of Computational and Graphical Statistics, vol.9, issue.2, pp.319-337, 2000.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of The Royal Statistical Society Series B, vol.58, issue.1, pp.267-288, 1996.

M. J. Wainwright, Sharp thresholds for noisy and high-dimensional recovery of sparsity using ? 1 -constrained quadratic programming, 2006.

F. T. Wright, A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables Whose Distributions are not Necessarily Symmetric, The Annals of Probability, vol.1, issue.6, pp.1068-1070, 1973.
DOI : 10.1214/aop/1176996815

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

M. Yuan and Y. Lin, On the non-negative garrotte estimator, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.101, issue.2, pp.143-161, 2007.
DOI : 10.1111/j.1467-9868.2005.00503.x

C. Zhang and J. Huang, The sparsity and bias of the Lasso selection in high-dimensional linear regression, The Annals of Statistics, vol.36, issue.4, pp.1567-1594, 2008.
DOI : 10.1214/07-AOS520

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

P. Zhao, G. Rocha, and B. Yu, Grouped and hierarchical model selection through composite absolute penalties, Annals of Statistics, 2008.

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

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