An Introduction to Continuity, Extrema, and Related Topics for General Gaussian Processes, IMS, 1990. ,
Convex multi-task feature learning, Machine Learning, pp.243-272, 2008. ,
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
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
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
High-dimensional non-linear variable selection through hierarchical kernel learning, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00413473
Model-Based Compressive Sensing, IEEE Transactions on Information Theory, vol.56, issue.4, 2008. ,
DOI : 10.1109/TIT.2010.2040894
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
Convex Analysis and Nonlinear Optimization: Theory and Examples, 2006. ,
Convex Optimization, 2004. ,
Combinatorics: Topics, Techniques, Algorithms, 1994. ,
Spectral Graph Theory, 1997. ,
DOI : 10.1090/cbms/092
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
Knowledge Spaces, 1998. ,
Least angle regression, Annals of statistics, vol.32, issue.2, pp.407-451, 2004. ,
Asymptotics for lasso-type estimators, The Annals of Statistics, vol.28, issue.5, pp.1356-1378, 2000. ,
DOI : 10.1214/aos/1015957397
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
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
Exploiting structure in wavelet-based Bayesian compressive sensing, IEEE Transactions on Signal Processing, vol.57, pp.3488-3497, 2009. ,
The benefit of group sparsity, The Annals of Statistics, vol.38, issue.4, 2009. ,
DOI : 10.1214/09-AOS778
Learning with structured sparsity, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009. ,
DOI : 10.1145/1553374.1553429
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
Structured sparse principal component analysis, International Conference on Artificial Intelligence and Statistics (AISTATS), 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00414158
Tree-guided group Lasso for multi-task regression with structured sparsity, 2009. ,
Efficient sparse coding algorithms, Advances in Neural Information Processing Systems, 2007. ,
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
Concentration Inequalities and Model Selection: Ecole d'´ eté de Probabilités de Saint- Flour 23, 2003. ,
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
Learning the kernel function via regularization, Journal of Machine Learning Research, vol.6, issue.2, p.1099, 2006. ,
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
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
On the Lasso and its dual, Journal of Computational and Graphical Statistics, vol.9, pp.319-337, 2000. ,
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
Convex Analysis, 1970. ,
DOI : 10.1515/9781400873173
Boosting as a regularized path to a maximum margin classifier, Journal of Machine Learning Research, vol.5, pp.941-973, 2004. ,
The Group-Lasso for generalized linear models, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008. ,
DOI : 10.1145/1390156.1390263
Morphological Image Analysis: Principles and Applications, 2003. ,
Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B, pp.267-288, 1996. ,
SDPT3?a MATLAB software package for semidefinite programming, version 1.3. Optimization Methods and Software, pp.545-581, 1999. ,
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
Solving semidefinite-quadratic-linear programs using SDPT3, Mathematical Programming, pp.189-217, 2003. ,
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
Boosting with spatial regularization, Advances in Neural Information Processing Systems, 2009. ,
A comparison of optimization methods for large-scale L1-regularized linear classification, 2009. ,
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
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
On model selection consistency of Lasso, Journal of Machine Learning Research, vol.7, pp.2541-2563, 2006. ,
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
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
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