Sparse prediction with the k-support norm, Advances in Neural Information Processing Systems 25, pp.1466-1474, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00858954
Structured sparsity-inducing norms through submodular functions, Adv. NIPS, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00511310
Learning with Submodular Functions: A Convex Optimization Perspective, Foundations and Trends?? in Machine Learning, vol.6, issue.2-3, pp.145-373, 2013. ,
DOI : 10.1561/2200000039
URL : https://hal.archives-ouvertes.fr/hal-00645271
Optimization with sparsity-inducing penalties. Foundation and Trends, Machine Learning, pp.1-106, 2012. ,
DOI : 10.1561/2200000015
URL : https://hal.archives-ouvertes.fr/hal-00613125
Model-Based Compressive Sensing, IEEE Transactions on Information Theory, vol.56, issue.4, pp.1982-2001, 2010. ,
DOI : 10.1109/TIT.2010.2040894
The Isotonic Regression Problem and its Dual, Journal of the American Statistical Association, vol.17, issue.9, pp.140-147, 1972. ,
DOI : 10.1080/01621459.1972.10481216
Absolute and monotonic norms, Numerische Mathematik, vol.63, issue.1, pp.257-264, 1961. ,
DOI : 10.1007/BF01386026
Active set algorithms for isotonic regression; A unifying framework, Mathematical Programming, pp.425-439, 1990. ,
DOI : 10.1007/BF01580873
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
A lasso for hierarchical interactions. The Annals of Statistics, pp.1111-1141, 2013. ,
DOI : 10.1214/13-aos1096
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527358
SLOPE???Adaptive variable selection via convex optimization, The Annals of Applied Statistics, vol.9, issue.3, pp.1103-1140, 2015. ,
DOI : 10.1214/15-AOAS842SUPP
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4689150
Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR, Biometrics, vol.67, issue.1, pp.115-123, 2008. ,
DOI : 10.1111/j.1541-0420.2007.00843.x
Convex Optimization, 2004. ,
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows, International Journal of Computer Vision, vol.40, issue.9, pp.288-307, 2009. ,
DOI : 10.1007/s11263-009-0238-9
URL : http://escholarship.org/uc/item/5sd211v1.pdf
The Convex Geometry of Linear Inverse Problems, Foundations of Computational Mathematics, vol.1, issue.10, pp.805-849, 2012. ,
DOI : 10.1007/s10208-012-9135-7
On Nonlinear Fractional Programming, Management Science, vol.13, issue.7, pp.492-498, 1967. ,
DOI : 10.1287/mnsc.13.7.492
Submodular Functions, Matroids, and Certain Polyhedra, Combinatorial optimization -Eureka, you shrink!, pp.11-26, 2003. ,
DOI : 10.1007/3-540-36478-1_2
Sparse estimation with strongly correlated variables using ordered weighted 1 regularization, 2014. ,
Submodular Functions and Optimization, 2005. ,
A Fast Parametric Maximum Flow Algorithm and Applications, SIAM Journal on Computing, vol.18, issue.1, pp.30-55, 1989. ,
DOI : 10.1137/0218003
Two algorithms for maximizing a separable concave function over a polymatroid feasible region, European Journal of Operational Research, vol.54, issue.2, pp.227-236, 1991. ,
DOI : 10.1016/0377-2217(91)90300-K
Exploiting structure in wavelet-based Bayesian compressive sensing, IEEE Transactions on Signal Processing, vol.57, pp.3488-3497, 2009. ,
About strongly polynomial time algorithms for quadratic optimization over submodular constraints, Mathematical Programming, vol.34, issue.3, pp.1-3269, 1995. ,
DOI : 10.1007/BF01585561
Learning with structured sparsity, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.3371-3412, 2011. ,
DOI : 10.1145/1553374.1553429
URL : http://arxiv.org/abs/0903.3002
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 variable selection with sparsity-inducing norms, JMLR, vol.12, pp.2777-2824, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00377732
Proximal methods for sparse hierarchical dictionary learning, Proc. ICML, 2010. ,
Proximal methods for hierarchical sparse coding, JMLR, vol.12, pp.2297-2334, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00516723
Tree-guided group lasso for multi-task regression with structured sparsity, Proc. ICML, 2010. ,
DOI : 10.1214/12-aoas549
URL : http://arxiv.org/abs/0909.1373
On the ratio of optimal integral and fractional covers, Discrete Mathematics, vol.13, issue.4, pp.383-390, 1975. ,
DOI : 10.1016/0012-365X(75)90058-8
Generalized Isotonic Regression, Journal of Computational and Graphical Statistics, vol.58, issue.1, pp.192-210, 2014. ,
DOI : 10.1016/0022-0000(83)90006-5
Convex and network flow optimization for structured sparsity, JMLR, vol.12, pp.2681-2720, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00584817
New perspectives on k-support and cluster norms. arXiv preprint, 2015. ,
Regularizers for structured sparsity, Advances in Computational Mathematics, vol.37, issue.6A, pp.455-489, 2013. ,
DOI : 10.1007/s10444-011-9245-9
A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers, Statistical Science, vol.27, issue.4, pp.538-557, 2012. ,
DOI : 10.1214/12-STS400SUPP
Joint support recovery under high-dimensional scaling: Benefits and perils of 1 -? -regularization, Adv. NIPS, 2008. ,
Group Lasso with overlaps: the Latent Group Lasso approach, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00628498
Algorithms for a Class of Isotonic Regression Problems, Algorithmica, vol.23, issue.3, pp.211-222, 1999. ,
DOI : 10.1007/PL00009258
Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978. ,
DOI : 10.1016/0005-1098(78)90005-5
Convex Analysis, 1970. ,
DOI : 10.1515/9781400873173
Matrix Perturbation Theory, 1990. ,
Isotonic Regression via Partitioning, Algorithmica, vol.33, issue.1, pp.93-112, 2013. ,
DOI : 10.1007/s00453-012-9628-4
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.310.1384
Weakly decomposable regularization penalties and structured sparsity, Scandinavian Journal of Statistics, vol.41, issue.1, pp.72-86, 2014. ,
DOI : 10.1111/sjos.12032
Hierarchical sparse modeling: A choice of two regularizers. arXiv preprint, 2015. ,
Structured variable selection and estimation, The Annals of Applied Statistics, vol.3, issue.4, pp.1738-1757, 2009. ,
DOI : 10.1214/09-AOAS254
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
The composite absolute penalties family for grouped and hierarchical variable selection. The Annals of Statistics, pp.3468-3497, 2009. ,
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
On model selection consistency of Lasso, JMLR, vol.7, pp.2541-2563, 2006. ,
Efficient Sparse Modeling With Automatic Feature Grouping, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.9, pp.1436-1447, 2012. ,
DOI : 10.1109/TNNLS.2012.2200262
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.2577
Exclusive lasso for multi-task feature selection, AISTATS, 2010. ,