Semi-supervised learning with an explicit label-error model for misclassified data, Proceedings of the 18th IJCAI, pp.555-560, 2003. ,
Generalization of l1 constraint for high-dimensional regression problems, 2008. ,
Information theory and an extension of the maximum likelihood principle Budapest: Akademia Kiado, 1973. [Alq08] P. Alquier. Lasso, iterative feature selection and the correlation selector: Oracle inequalities and numerical performances, 2nd International Symposium on Information Theory, pp.267-281, 2008. ,
A framework for learning predictive structures from multiple tasks and unlabeled data, J. Mach. Learn. Res, vol.6, pp.1817-1853, 2005. ,
Consistency of the group lasso and multiple kernel learning, J. Mach. Learn. Res, vol.9, pp.1179-1225, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00164735
Person identification in webcam images: an application of semisupervised learning, ICML Workshop on Learning with Partially Classified Training Data, 2005. ,
Combining labeled and unlabeled data with cotraining, Proceedings of the 11th Annual Conference on Computational Learning Theory, pp.92-100, 1998. ,
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
Predicting Gene Expression from Sequence, Cell, vol.117, issue.2, pp.185-198, 2004. ,
DOI : 10.1016/S0092-8674(04)00304-6
Aggregation for Gaussian regression, The Annals of Statistics, vol.35, issue.4 ,
DOI : 10.1214/009053606000001587
URL : http://arxiv.org/abs/0710.3654
Consistent selection via the Lasso for high dimensional approximating regression models, IMS Collections, vol.3, 2008. ,
PAC-Bayesian Supervised Classification (The Thermodynamics of Statistical Learning), volume 56 of Lecture Notes-Monograph Series. IMS Some theoretical results on the grouped variables lasso, Mathematical Methods of Statistics, vol.17, issue.4, pp.317-326, 2007. ,
Integrating regulatory motif discovery and genome-wide expression analysis, Proceedings of the National Academy of Science, pp.3339-3344, 2003. ,
DOI : 10.1073/pnas.0630591100
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC152294
Unsupervised models for named entity classification, Proc. Joint SIGDAT Conf. on Empirical Methods in Natural Language Processing and Very Large Corpora, pp.100-110, 1999. ,
Semi-supervised learning, 2006. ,
DOI : 10.7551/mitpress/9780262033589.001.0001
The dantzig selector: statistical estimation when p is much larger than n, Ann. Statist, vol.35, 2007. ,
Semi-supervised classification by low density separation, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pp.57-64, 2005. ,
Aggregation by Exponential Weighting and Sharp Oracle Inequalities, Proceedings. Lecture Notes in Computer Science, vol.4539, pp.97-111, 2007. ,
DOI : 10.1007/978-3-540-72927-3_9
URL : https://hal.archives-ouvertes.fr/hal-00160857
Pathwise coordinate optimization, The Annals of Applied Statistics, vol.1, issue.2, pp.302-332, 2007. ,
DOI : 10.1214/07-AOAS131
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001. ,
DOI : 10.1198/016214501753382273
Regularization with the smooth-lasso procedure, 2008. ,
Transductive inference for text classification using support vector machines, ICML, 1999. ,
DASSO: connections between the Dantzig selector and lasso, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.1, pp.127-142, 2009. ,
DOI : 10.1111/j.1467-9868.2008.00668.x
An interior-point method for large-scale l1-regularized least squares, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.606-617, 2007. ,
The Dantzig selector and sparsity oracle inequalities, Bernoulli, vol.15, issue.3, pp.799-828, 2009. ,
DOI : 10.3150/09-BEJ187
Sparse recovery in convex hulls via entropy penalization, The Annals of Statistics, vol.37, issue.3, pp.1332-1359, 2009. ,
DOI : 10.1214/08-AOS621
Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators, Electron. J. Stat, vol.2, pp.90-102, 2008. ,
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
Smoothing ??? 1 -penalized estimators for high-dimensional time-course data, Electronic Journal of Statistics, vol.1, issue.0, pp.597-615, 2007. ,
DOI : 10.1214/07-EJS103
High-dimensional additive modeling, Ann. Statist, vol.37, issue.6B, pp.3779-3821, 2009. ,
Lasso-type recovery of sparse representations for high-dimensional data, The Annals of Statistics, vol.37, issue.1, pp.246-270, 2009. ,
DOI : 10.1214/07-AOS582
Text classification from labeled and unlabeled documents using em, In Mach. Learn, pp.103-134, 1999. ,
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Regression shrinkage and selection via the lasso, J. Roy. Statist. Soc. Ser. B, vol.58, issue.1, pp.267-288, 1996. ,
The Nature of Statistical Learning Theory Statistical Learning Theory High-dimensional generalized linear models and the lasso On the conditions used to prove oracle results for the lasso, Ann. Statist. Elect. Journ. Statist, vol.36, issue.3, pp.614-6451360, 1998. ,
Sharp thresholds for noisy and high-dimensional recovery of sparsity using l1-constrained quadratic programming, 2006. ,
On transductive support vector machines, Prediction and discovery, pp.7-19, 2007. ,
DOI : 10.1090/conm/443/08551
Semi-supervised learning using gaussian fields and harmonic functions, ICML, 2003. ,
GENE FUNCTION PREDICTION BY A COMBINED ANALYSIS OF GENE EXPRESSION DATA AND PROTEIN-PROTEIN INTERACTION DATA, Journal of Bioinformatics and Computational Biology, vol.03, issue.06, pp.1371-1389, 2005. ,
DOI : 10.1142/S0219720005001612
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
Learning with local and global consistency, NIPS 16, 2003. ,
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
On model selection consistency of Lasso, J. Mach. Learn. Res, vol.7, pp.2541-2563, 2006. ,