M. S. Anderson, J. Dahl, and L. Vandenberghe, CVXOPT: A Python package for convex optimization, 2012.

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with Sparsity-Inducing Penalties, Machine Learning, pp.1-106, 2012.
DOI : 10.1561/2200000015
URL : https://hal.archives-ouvertes.fr/hal-00613125

P. L. Bartlett and S. Mendelson, Rademacher and Gaussian Complexities: Risk Bounds and Structural Results, Journal of Machine Learning Research, vol.3, pp.463-482, 2002.
DOI : 10.1007/3-540-44581-1_15

H. H. Bauschke and P. L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00643354

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, 1992.
DOI : 10.1145/130385.130401

S. Boucheron, O. Bousquet, and G. Lugosi, Theory of Classification: a Survey of Some Recent Advances, ESAIM: Probability and Statistics, vol.49, pp.323-375, 2005.
DOI : 10.1109/TIT.2003.813564
URL : https://hal.archives-ouvertes.fr/hal-00017923

C. Brouard, M. Szafranski, and F. Buc, Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels, Journal of Machine Learning Research, vol.17, issue.176, pp.1-48, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01216708

H. Drucker, C. J. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, Support Vector Regression Machines, Advances in Neural Information Processing Systems 9, 1997.

M. Farooq and I. Steinwart, An SVM-like approach for expectile regression, Computational Statistics & Data Analysis, vol.109, p.2015
DOI : 10.1016/j.csda.2016.11.010

M. Farooq and I. Steinwart, Learning Rates for Kernel-Based Expectile Regression, p.2017

O. Fercoq and P. Bianchi, A Coordinate Descent Primal-Dual Algorithm with Large Step Size and Possibly Non Separable Functions, p.2015
URL : https://hal.archives-ouvertes.fr/hal-01497104

T. Hastie, R. Tibshirani, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, 2009.

T. Hastie, R. Tibshirani, and M. Wainwright, Statistical Learning with Sparsity: The Lasso and Generalizations, 2015.
DOI : 10.1201/b18401

R. Koenker, Quantile Regression, 2005.

N. Lim, F. Buc, C. Auliac, and G. Michailidis, Operator-valued kernel-based vector autoregressive models for network inference, Machine Learning, pp.489-513, 2014.
DOI : 10.1186/1471-2105-10-122
URL : https://hal.archives-ouvertes.fr/hal-00872342

C. A. Micchelli and M. A. , On Learning Vector-Valued Functions, Neural Computation, vol.1, issue.1, pp.177-204, 2005.
DOI : 10.1109/34.735807

H. Q. Minh, L. Bazzani, and V. Murino, A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning, Journal of Machine Learning Research, vol.17, issue.25, pp.1-72, 2016.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2012.

E. Ndiaye, O. Fercoq, A. Gramfort, and J. Salmon, GAP Safe screening rules for sparse multi-task and multi-class models, Advances in Neural Information Processing Systems 28, p.2015
URL : https://hal.archives-ouvertes.fr/hal-02287197

E. Ndiaye, O. Fercoq, A. Gramfort, and J. Salmon, GAP Safe Screening Rules for Sparse- Group Lasso, Advances in Neural Information Processing Systems 29, 2016.

W. K. Newey and J. L. Powell, Asymmetric Least Squares Estimation and Testing, Econometrica, vol.55, issue.4, pp.819-847, 1987.
DOI : 10.2307/1911031

J. Park and J. Kim, Quantile regression with an epsilon-insensitive loss in a reproducing kernel Hilbert space, Statistics & Probability Letters, vol.81, issue.1, pp.62-70, 2011.
DOI : 10.1016/j.spl.2010.09.019

J. C. Platt, Fast training of support vector machines using sequential minimal optimization Advances in Kernel Methods, 1999.

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

M. Sangnier, O. Fercoq, and F. Buc, Joint quantile regression in vector-valued RKHSs, Advances in Neural Information Processing Systems 29, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01272327

B. Schölkopf, R. Herbrich, and A. J. Smola, A Generalized Representer Theorem, Computational Learning Theory, pp.416-426, 2001.
DOI : 10.1007/3-540-44581-1_27

S. Shalev-shwartz and T. Zhang, Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization, Journal of Machine Learning Research, vol.14, pp.567-599, 2013.

A. Shibagaki, M. Karasuyama, K. Hatano, and I. Takeuchi, Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling, Proceedings of the 33rd International Conference on International Conference on Machine Learning, 2016.

I. Steinwart and A. Christmann, Support Vector Machines, 2008.

I. Takeuchi, Q. V. Le, T. D. Sears, and A. J. Smola, Nonparametric Quantile Estimation, Journal of Machine Learning Research, vol.7, pp.1231-1264, 2006.

I. Takeuchi, T. Hongo, M. Sugiyama, and S. Nakajima, Parametric Task Learning, Advances in Neural Information Processing Systems 26, pp.1358-1366, 2013.

R. Tibshirani, Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

A. N. Tikhonov and V. Y. Arsenin, Solutions of ill-posed problems, 1977.

M. E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, vol.1, pp.211-244, 2001.

V. Vapnik, The Nature of Statistical Learning Theory. Information Science and Statistics, 2010.

Y. Yang, T. Zhang, and H. Zou, Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces, Technometrics, vol.55, issue.1, p.2015
DOI : 10.1111/mafi.12080

C. Zhang, Y. Liu, and Y. Wu, On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint, Journal of Machine Learning Research, vol.17, issue.1, pp.1374-1418, 2016.

, References

M. A. Alvarez, L. Rosasco, and N. D. Lawrence, Kernels for Vector-Valued Functions: A Review, Machine Learning, pp.195-266, 2012.
DOI : 10.1561/2200000036

S. Boucheron, G. Lugosi, and P. Massart, Concentration Inequalities: A Nonasymptotic Theory of Independence
DOI : 10.1093/acprof:oso/9780199535255.001.0001
URL : https://hal.archives-ouvertes.fr/hal-00794821

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

A. Maurer, A Vector-Contraction Inequality for Rademacher Complexities, Proceedings of The 27th International Conference on Algorithmic Learning Theory, 2016.
DOI : 10.1002/j.1538-7305.1962.tb02419.x
URL : http://arxiv.org/pdf/1605.00251

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2012.

M. Sangnier, O. Fercoq, and F. Buc, Joint quantile regression in vector-valued RKHSs, Advances in Neural Information Processing Systems 29, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01272327

I. Takeuchi, Q. V. Le, T. D. Sears, and A. J. Smola, Nonparametric Quantile Estimation, Journal of Machine Learning Research, vol.7, pp.1231-1264, 2006.