A. Argyriou, T. Evgeniou, and M. Pontil, Convex multi-task feature learning, Machine Learning, pp.243-272, 2008.
DOI : 10.1007/s10994-007-5040-8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.2025

F. Bach, Consistency of trace norm minimization, JMLR, vol.9, pp.1019-1048, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00179522

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

A. Berg, J. Deng, and F. Li, ImageNet large scale visual recognition challenge, 2010.

D. Bertsekas, Nonlinear Programming, Athena Scientific, 2004.

S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge UP, 2004.

V. Chandrasekaran, Convex Optimization Methods for Graphs and Statistical Modeling, 2011.

O. Chapelle and Z. Harchaoui, A machine learning approach to conjoint analysis, Adv. NIPS, 2005.

K. L. Clarkson, Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm, Proc. SODA, 2008.
DOI : 10.1145/1824777.1824783

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.9299

M. Collins, R. E. Schapire, and Y. Singer, Logistic regression, AdaBoost and Bregman distances, Machine Learning, p.47, 2002.

A. Demiriz, K. P. Bennett, and J. Shawe-taylor, Linear programming boosting via column generation, Machine Learning, 2002.

V. Demyanov and A. Rubinov, Approximate Methods in Optimization Problems, 1970.

M. Dudík, S. J. Phillips, and R. E. Schapire, Maximum entropy density estimation with generalized regularization and an application to species distribution modeling, JMLR, vol.8, pp.1217-1260, 2007.

M. Fazel, Matrix rank minimization with applications, 2002.

M. Frank and P. Wolfe, An algorithm for quadratic programming, Naval Research Logistics Quarterly, vol.3, issue.1-2, pp.95-110, 1956.
DOI : 10.1002/nav.3800030109

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, 2010.
DOI : 10.18637/jss.v033.i01

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning

S. Series and I. Statistics, [19] E. Hazan. Sparse approximate solutions to semidefinite programs, Proc. 8th Latin American Conf. Theor. Informatics, pp.306-316, 2008.

J. Hiriart-urruty and C. Lemaréchal, Convex Analysis and Minimization Algorithms, 1993.
DOI : 10.1007/978-3-662-02796-7

E. J. Cai and Z. Shen, A Singular Value Thresholding Algorithm for Matrix Completion, SIAM Journal on Optimization, vol.20, issue.4, pp.1956-1982, 2008.
DOI : 10.1137/080738970

M. Jaggi and M. Sulovsk´ysulovsk´y, A simple algorithm for nuclear norm regularized problems, ICML, 2010.

G. Jameson, Summing and nuclear norms in Banach space theory, 1987.
DOI : 10.1017/CBO9780511569166

S. Ji and J. Ye, An accelerated gradient method for trace norm minimization, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553434

L. Mason, P. Bartlett, J. Baxter, and M. Frean, Functional gradient techniques for combining hypotheses, Adv. Large Margin Classifiers, 2000.

Y. Nesterov, Gradient methods for minimizing composite objective function, CORE, 2007.

Y. Nesterov, Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems, SIAM Journal on Optimization, vol.22, issue.2, 2010.
DOI : 10.1137/100802001

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, 2010.
DOI : 10.1007/s11222-008-9111-x

R. Phelps, Convex functions, monotone operators , and differentiability. Lecture notes in mathematics, 1993.
DOI : 10.1007/978-3-662-21569-2

T. K. Pong, S. J. Tseng, and J. Ye, Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning, SIAM Journal on Optimization, vol.20, issue.6, pp.3465-3489, 2010.
DOI : 10.1137/090763184

B. Recht, M. Fazel, and P. Parrilo, Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization, SIAM Review, vol.52, issue.3, pp.471-501, 2010.
DOI : 10.1137/070697835

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

S. Shalev-shwartz, A. Gonen, and O. Shamir, Large-scale convex minimization with a low-rank constraint, ICML, 2011.

W. Shi, G. Wahba, S. Wright, K. Lee, R. Klein et al., Lasso-patternsearch algorithm with application to ophthalmology and genomic data, ASA Proceedings of the Joint Statistical Meetings, 2006.

S. Sra, S. Nowozin, and S. J. Wright, Optimization for Machine Learning, 2010.

N. Srebro, J. D. Rennie, and T. S. Jaakola, Maximum-margin matrix factorization, Adv. NIPS, 2005.

A. Tewari, P. K. Ravikumar, and I. S. Dhillon, Greedy algorithms for structurally constrained high dimensional problems, Adv. NIPS, 2011.

S. Wright, Accelerated Block-coordinate Relaxation for Regularized Optimization, SIAM Journal on Optimization, vol.22, issue.1, 2010.
DOI : 10.1137/100808563

G. Yuan, K. Chang, C. Hsieh, and C. Lin, A comparison of optimization methods and software for large-scale l1-regularized linear classification, JMLR, vol.11, 2010.

T. Zhang, Sequential greedy approximation for certain convex optimization problems, IEEE Transactions on Information Theory, vol.49, issue.3, pp.682-691, 2003.
DOI : 10.1109/TIT.2002.808136