A. Antoniadis-for-garvesh-raskutti, M. J. Wainwright, and B. Yu, Minimax-optimal rates for sparse additive models over kernel classes via convex programming, J. Mach. Learn. Res, vol.13, pp.1-389, 2012.

P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman, Sparse additive models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.101, issue.5, pp.1009-1030, 2009.
DOI : 10.1111/j.1467-9868.2009.00718.x

N. Simon and R. Tibshirani, Standardization and the Group Lasso Penalty, Statistica Sinica, vol.22, issue.3, pp.983-1001, 2012.
DOI : 10.5705/ss.2011.075

N. Städler, P. Bühlmann, and S. Van-de-geer, ???1-penalization for mixture regression models, TEST, vol.101, issue.2, pp.209-256, 2010.
DOI : 10.1007/s11749-010-0197-z

F. Jos and . Sturm, Using sedumi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optimization Methods and Software, pp.11-12625, 1999.

T. Sun and C. Zhang, Comments on: ??? 1-penalization for mixture regression models, TEST, vol.38, issue.2, pp.270-275, 2010.
DOI : 10.1007/s11749-010-0201-7

T. Sun and C. Zhang, Scaled sparse linear regression, Biometrika, vol.99, issue.4, pp.879-898, 2012.
DOI : 10.1093/biomet/ass043

R. Tibshirani, Regression shrinkage and selection via the Lasso, J. Roy. Statist. Soc. Ser. B, vol.58, issue.1, pp.267-288, 1996.

J. Wagener and H. Dette, Bridge estimators and the adaptive lasso under heteroscedasticity, Mathematical Methods of Statistics, vol.21, issue.2, pp.109-126, 2012.
DOI : 10.3103/S1066530712020032

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
DOI : 10.1198/016214502753479356