Faster independent component analysis by preconditioning with Hessian approximations, IEEE Trans. Signal Process, vol.66, issue.15, p.33, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01552340
Multi-task feature learning, NeurIPS, p.24, 2006. ,
Combined EEG/MEG can outperform single modality EEG or MEG source reconstruction in presurgical epilepsy diagnosis, PloS one, vol.10, issue.3, p.33, 2015. ,
Convex optimization with sparsityinducing norms. Foundations and Trends in Machine Learning, vol.4, p.44, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00937150
Convex analysis and monotone operator theory in Hilbert spaces, vol.27, p.53, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01517477
First-Order Methods in Optimization, vol.25, p.100, 2017. ,
A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM J. Imaging Sci, vol.2, issue.1, p.45, 2009. ,
Gradient-based algorithms with applications to signal-recovery problems, p.24, 2010. ,
Smoothing and first order methods: A unified framework, SIAM J. Optim, vol.22, issue.2, p.97, 2012. ,
Templates for convex cone problems with applications to sparse signal recovery, Math. Program. Comput, vol.3, issue.3, p.86, 2011. ,
, The best of both worlds. Computing in Science Engineering, vol.13, p.78, 2011.
Square-root Lasso: pivotal recovery of sparse signals via conic programming, Biometrika, vol.98, issue.4, p.125, 2011. ,
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso, NeurIPS, vol.90, p.103, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02010014
Simultaneous analysis of Lasso and Dantzig selector, Ann. Statist, vol.37, issue.4, p.86, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00401585
The group fused lasso for multiple change-point detection, p.26, 2011. ,
Active set strategy for highdimensional non-convex sparse optimization problems, ICASSP, vol.44, p.75, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01025585
Nonlinear acceleration of momentum and primal-dual algorithms, p.51, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01893921
A dynamic screening principle for the lasso, EUSIPCO, vol.55, p.66, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00880787
Dynamic screening: accelerating first-order algorithms for the Lasso and Group-Lasso, IEEE Trans. Signal Process, vol.63, issue.19, p.55, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01084986
Convex optimization, vol.91, p.105, 2004. ,
Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, vol.3, p.24, 2011. ,
High-dimensional variable screening and bias in subsequent inference, with an empirical comparison, Computational Statistics, vol.29, issue.3, p.130, 2014. ,
API design for machine learning software: experiences from the scikit-learn project, p.78, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00856511
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, vol.52, issue.2, p.26, 2006. ,
Enhancing sparsity by reweighted l 1 minimization, J. Fourier Anal. Applicat, vol.14, issue.5-6, p.25, 2008. ,
A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imaging Vis, vol.40, issue.1, p.101, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00490826
Exact reconstruction of sparse signals via nonconvex minimization, IEEE Trans. Signal Process. Lett, vol.14, issue.10, p.24, 2007. ,
Alternating estimation for structured high-dimensional multiresponse models, NeurIPS, vol.87, p.107, 2017. ,
Atomic decomposition by basis pursuit, SPIE, vol.24, p.44, 1995. ,
Atomic decomposition by basis pursuit, SIAM J. Sci. Comput, vol.20, issue.1, p.23, 1998. ,
Robust modeling with erratic data, Geophysics, vol.38, issue.5, p.23, 1973. ,
Magnetoencephalography: evidence of magnetic fields produced by alpharhythm currents, Science, vol.161, issue.3843, p.33, 1968. ,
Proximal splitting methods in signal processing. In Fixed-point algorithms for inverse problems in science and engineering, Springer Optim. Appl, vol.49, p.29, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00643807
Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity, Neuron, vol.26, issue.1, p.36, 2000. ,
High-dimensional heteroscedastic regression with an application to eQTL data analysis, Biometrics, vol.68, issue.1, p.86, 2012. ,
Compressed sensing, IEEE Trans. Inf. Theory, vol.52, issue.4, p.26, 2006. ,
URL : https://hal.archives-ouvertes.fr/inria-00369486
Ideal spatial adaptation by wavelet shrinkage, Biometrika, vol.81, issue.3, p.23, 1994. ,
The future of ultra-high field MRI and fMRI for study of the human brain, Neuroimage, vol.62, issue.2, p.31, 2012. ,
An algorithm for restricted least squares regression, J. Amer. Statist. Assoc, vol.78, issue.384, p.53, 1983. ,
Multiple regression analysis, Mathematical methods for digital computers, p.23, 1960. ,
Safe feature elimination in sparse supervised learning, J. Pacific Optim, vol.8, issue.4, p.94, 2012. ,
Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, NeuroImage, vol.108, p.87, 2015. ,
Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, NeuroImage, vol.108, p.34, 2015. ,
Mind the noise covariance when localizing brain sources with M/EEG, Pattern Recognition in NeuroImaging (PRNI), p.34, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01183551
Liblinear: A library for large linear classification, J. Mach. Learn. Res, vol.9, p.66, 2008. ,
Variable selection via nonconcave penalized likelihood and its oracle properties, J. Amer. Statist. Assoc, vol.96, issue.456, p.24, 2001. ,
Sure independence screening for ultrahigh dimensional feature space, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.70, issue.5, p.94, 2008. ,
Matrix rank minimization with applications, p.24, 2002. ,
Accelerated, parallel and proximal coordinate descent, SIAM J. Optim, vol.25, issue.3, p.67, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-02287265
Mind the duality gap: safer rules for the lasso, ICML, vol.55, p.86, 2015. ,
A statistical view of some chemometrics regression tools, Technometrics, vol.35, issue.2, p.24, 1993. ,
Pathwise coordinate optimization, Ann. Appl. Stat, vol.1, issue.2, p.66, 2007. ,
Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, p.106, 2008. ,
Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw, vol.33, issue.1, p.78, 2010. ,
Penalized regressions: the bridge versus the lasso, J. Comput. Graph. Statist, vol.7, issue.3, p.44, 1998. ,
Recovering sparse signals with a certain family of nonconvex penalties and DC programming, IEEE Trans. Signal Process, vol.57, issue.12, p.25, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00439453
, Theoria motus corporum coelestium in sectionibus conicis solem ambientium, vol.7, pp.1809-1830
Neuronal generators and the problem of localization in electroencephalography: application of volume conductor theory to electroencephalography, Journal of clinical neurophysiology, vol.2, issue.4, p.31, 1985. ,
On convergence rates of subgradient optimization methods, Mathematical Programming, vol.13, issue.1, p.29, 1977. ,
Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Phys. Med. Biol, vol.57, issue.7, p.81, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00690774
Timefrequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations, NeuroImage, vol.70, p.130, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00773276
MNE software for processing MEG and EEG data, NeuroImage, vol.86, p.130, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-02369299
Good practice for conducting and reporting MEG research, NeuroImage, vol.65, p.33, 2013. ,
Fixed-point continuation for 1 -minimization: Methodology and convergence, SIAM J. Optim, vol.19, issue.3, p.71, 2008. ,
The Elements of Statistical Learning, Springer Series in Statistics, p.17, 2009. ,
Surprises in highdimensional ridgeless least squares interpolation, p.22, 2019. ,
Combining sparsity and rotational invariance in EEG/MEG source reconstruction, NeuroImage, vol.42, issue.2, p.130, 2008. ,
Convex analysis and minimization algorithms, vol.II, p.27, 1993. ,
Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, vol.12, issue.1, p.22, 1970. ,
Scaling up sparse support vector machines by simultaneous feature and sample reduction, J. Mach. Learn. Res, vol.20, issue.121, p.44, 2019. ,
QUIC: Quadratic approximation for sparse inverse covariance estimation, J. Mach. Learn. Res, vol.15, p.78, 2014. ,
A selective review of group selection in highdimensional models. Statistical science: a review journal of the Institute of Mathematical, Statistics, vol.27, issue.4, pp.2012-2036 ,
Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography, Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, vol.108, issue.1, p.35, 1998. ,
High-resolution CBV-fMRI allows mapping of laminar activity and connectivity of cortical input and output in human M1, Neuron, vol.96, issue.6, p.31, 2017. ,
Robust Statistics, p.86, 1981. ,
Numerical solution of robust regression problems, Compstat 1974 (Proc. Sympos. Computational Statist., Univ. Vienna, p.86, 1974. ,
Interpreting magnetic fields of the brain: minimum norm estimates, Medical & biological engineering & computing, vol.32, issue.1, p.35, 1994. ,
The theory of approximate methods and their applications to the numerical solution of singular integral equations, vol.2, p.22, 1976. ,
Scaling Machine Learning via Prioritized Optimization, p.60, 2018. ,
Blitz: A principled meta-algorithm for scaling sparse optimization, ICML, vol.66, p.94, 2015. ,
A fast, principled working set algorithm for exploiting piecewise linear structure in convex problems, vol.66, p.75, 2018. ,
Efficient greedy coordinate descent for composite problems, p.66, 2018. ,
An interior-point method for large-scale 1 -regularized least squares, IEEE J. Sel. Topics Signal Process, vol.1, issue.4, p.60, 2007. ,
Fast active-set-type algorithms for l1-regularized linear regression, AISTATS, p.56, 2010. ,
Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC: I: Principles and simulation study, Clinical Neurophysiology, vol.113, issue.5, p.35, 2002. ,
An interior-point method for large-scale l1-regularized logistic regression, J. Mach. Learn. Res, vol.8, issue.8, p.66, 2007. ,
Variance function estimation in high-dimensions, ICML, p.86, 2012. ,
EEG source localization: implementing the spatio-temporal decomposition approach, Electroencephalography and clinical Neurophysiology, vol.107, issue.5, p.35, 1998. ,
Accelerating ISTA with an active set strategy, OPT 2011: 4th International Workshop on Optimization for Machine Learning, vol.66, p.74, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00696992
Numba: A LLVM-based Python JIT Compiler, Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, vol.78, p.107, 2015. ,
A well-conditioned estimator for large-dimensional covariance matrices, J. Multivariate Anal, vol.88, issue.2, p.89, 2004. ,
Proximal Newton-type methods for convex optimization, NeurIPS, p.75, 2012. ,
Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood, vol.111, p.107, 2012. ,
, Nouvelles méthodes pour la détermination des orbites des comètes. F. Didot, 1805, p.21
A note on the lasso and related procedures in model selection, Statistica Sinica, vol.16, issue.4, p.25, 2006. ,
Estimation statistique en grande dimension, parcimonie et inégalités d'oracle, p.25, 2009. ,
Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents, Neuroimage, vol.61, issue.4, p.36, 2012. ,
Sparse coding for machine learning, image processing and computer vision, vol.46, p.69, 2010. ,
Independent component analysis of electroencephalographic data, NeurIPS, p.33, 1996. ,
Matching pursuit with time-frequency dictionaries, IEEE Trans. Image Process, vol.41, p.23, 1993. ,
Portfolio selection, The Journal of Finance, vol.7, issue.1, p.23, 1952. ,
From safe screening rules to working sets for faster lasso-type solvers, NIPS-OPT workshop, 2017. ,
Generalized concomitant multitask Lasso for sparse multimodal regression, AISTATS, vol.103, p.107, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01812011
Celer: a fast solver for the Lasso with dual extrapolation, ICML, pp.3321-3330, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01833398
Dual extrapolation for sparse Generalized Linear Models, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02263500
Selective minimum-norm solution of the biomagnetic inverse problem, IEEE Transactions on Biomedical Engineering, vol.42, issue.6, p.36, 1995. ,
Generalized Linear Models, CRC Monographs on Statistics and Applied Probability Series, vol.18, p.66, 1989. ,
High-dimensional graphs and variable selection with the lasso, Ann. Statist, vol.34, issue.3, p.25, 2006. ,
Subset selection in regression, p.23, 2002. ,
Insights and algorithms for the multivariate square-root lasso, vol.96, p.97, 2019. ,
Proximité et dualité dans un espace hilbertien, Bull. Soc. Math. France, vol.93, p.27, 1965. ,
Methods for solving incorrectly posed problems, vol.2, p.22, 1984. ,
Source localization using recursively applied and projected (RAP) MUSIC, IEEE Trans. Signal Process, vol.47, issue.2, p.35, 1999. ,
EEG source localization and imaging using multiple signal classification approaches, Journal of Clinical Neurophysiology, vol.16, issue.3, p.35, 1999. ,
Invariance in current dipole moment density across brain structures and species: Physiological constraint for neuroimaging, NeuroImage, vol.111, p.32, 2015. ,
A constraint selection technique for a class of linear programs, Operations Research Letters, vol.7, issue.4, p.44, 1988. ,
Autoregressive process modeling via the lasso procedure, Journal of Multivariate Analysis, vol.102, issue.3, p.26, 2011. ,
Sparse approximate solutions to linear systems, SIAM J. Comput, vol.24, issue.2, p.23, 1995. ,
Gap safe screening rules for sparse multi-task and multi-class models, NeurIPS, p.81, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-02287197
Efficient smoothed concomitant lasso estimation for high dimensional regression, Journal of Physics: Conference Series, vol.904, issue.1, p.101, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01404966
Gap safe screening rules for sparsity enforcing penalties, JMLR, vol.18, issue.128, p.94, 2017. ,
A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers, p.24, 2010. ,
A method for solving a convex programming problem with rate of convergence O(1/k 2 ), Soviet Math. Doklady, vol.269, issue.3, p.29, 1983. ,
Smooth minimization of non-smooth functions, Math. Program, vol.103, issue.1, p.97, 2005. ,
Relationship between the optimal solutions of least squares regularized with 0 -norm and constrained by k-sparsity, Applied and Computational Harmonic Analysis, vol.41, issue.1, p.24, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-00944006
Electric fields of the brain: the neurophysics of EEG, p.32, 2006. ,
Active-set complexity" of proximal gradient: how long does it take to find the sparsity pattern? Optimization Letters, p.69, 2017. ,
Joint covariate selection and joint subspace selection for multiple classification problems, Statistics and Computing, vol.20, issue.2, p.126, 2010. ,
On iteratively reweighted algorithms for nonsmooth nonconvex optimization in computer vision, SIAM J. Imaging Sci, vol.8, issue.1, p.25, 2015. ,
Quaestiones et decisiones in quatuor libros Sententiarum cum centilogio theologico, pp.1319-1341 ,
Safe screening of non-support vectors in pathwise SVM computation, ICML, vol.44, p.66, 2013. ,
Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision research, vol.37, p.26, 1997. ,
A distributed spatio-temporal EEG/MEG inverse solver, NeuroImage, vol.44, issue.3, p.130, 2009. ,
A robust hybrid of lasso and ridge regression, Cont. Math, vol.443, p.88, 2007. ,
Nonlinear optimization by successive linear programming, Management Science, vol.28, issue.10, p.44, 1982. ,
, Proximal algorithms. Foundations and Trends in Machine Learning, vol.1, p.29, 2013.
MEG: An Introduction to Methods, p.33, 2010. ,
Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, Methods Find. Exp. Clin. Pharmacol, vol.24, p.36, 2002. ,
Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar conference on signals, systems and computers, p.23, 1993. ,
Scikit-learn: Machine learning in Python, J. Mach. Learn. Res, vol.12, p.106, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Faster coordinate descent via adaptive importance sampling, AISTATS, vol.56, p.66, 2017. ,
Sufficient statistics and intrinsic accuracy, Mathematical Proceedings of the Cambridge Philosophical society, vol.32, p.19, 1936. ,
Studies in the History of Probability and Statistics. XXIX: The discovery of the method of least squares, Biometrika, vol.59, issue.2, p.21, 1972. ,
Local convergence properties of SAGA/Prox-SVRG and acceleration, ICML, p.69, 2018. ,
Simultaneously leveraging output and task structures for multiple-output regression, NeurIPS, p.87, 2012. ,
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function, Mathematical Programming, vol.144, issue.1-2, p.66, 2014. ,
Recent advances in biomagnetism, p.35, 1999. ,
, Convex analysis. Princeton Landmarks in Mathematics, p.28, 1997.
Boosting as a regularized path to a maximum margin classifier, J. Mach. Learn. Res, vol.5, p.69, 2004. ,
The group-lasso for generalized linear models: uniqueness of solutions and efficient algorithms, ICML, p.66, 2008. ,
Sparse multivariate regression with covariance estimation, Journal of Computational and Graphical Statistics, vol.19, issue.4, p.107, 2010. ,
A fast active set block coordinate descent algorithm for 1 -regularized least squares, SIAM J. Optim, vol.26, issue.1, p.75, 2016. ,
Linear inversion of band-limited reflection seismograms, SIAM Journal on Scientific and Statistical Computing, vol.7, issue.4, p.23, 1986. ,
, Complexity of inexact proximal Newton methods, p.75, 2013.
Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, vol.62, p.35, 1985. ,
Acceleration in Optimization, p.47, 2018. ,
URL : https://hal.archives-ouvertes.fr/tel-01887163
Regularized nonlinear acceleration, Neur-IPS, vol.51, p.121, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01384682
Understanding machine learning: From theory to algorithms, p.17, 2014. ,
Simultaneous safe screening of features and samples in doubly sparse modeling, ICML, p.44, 2016. ,
A sparse-group lasso, J. Comput. Graph. Statist, vol.22, issue.2, p.66, 2013. ,
A continuous exact 0 penalty (CEL0) for least squares regularized problem, SIAM J. Imaging Sci, vol.8, issue.3, p.86, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-01102492
Safe adaptive importance sampling, NeurIPS, p.56, 2017. ,
Spatio-Temporal Sparse Priors for MEG/EEG Source Reconstruction, p.36, 2016. ,
The iterative reweighted mixed-norm estimate for spatio-temporal MEG/EEG source reconstruction, IEEE Trans. Med. Imag, p.36, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01079530
Asymptotic confidence regions and sharp oracle results under structured sparsity, p.96, 2017. ,
Scaled sparse linear regression, Biometrika, vol.99, issue.4, p.88, 2012. ,
Are we there yet? Manifold identification of gradient-related proximal methods, AISTATS, p.69, 2019. ,
Deconvolution with the 1 norm, Geophysics, vol.44, issue.1, p.23, 1979. ,
Techniques for removing nonbinding constraints and extraneous variables from linear programming problems, Management Science, vol.12, issue.7, p.44, 1966. ,
Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.58, issue.1, p.86, 1996. ,
Strong rules for discarding predictors in lasso-type problems, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.74, issue.2, p.94, 2012. ,
The lasso problem and uniqueness, Electron. J. Stat, vol.7, p.70, 2013. ,
Dykstra's algorithm, ADMM, and coordinate descent: Connections, insights, and extensions, NeurIPS, vol.50, p.53, 2017. ,
On the stability of inverse problems, Dokl. Akad. Nauk SSSR, vol.39, p.22, 1943. ,
Just relax: convex programming methods for identifying sparse signals in noise, IEEE Trans. Inf. Theory, vol.52, issue.3, p.23, 2006. ,
Convergence of a block coordinate descent method for nondifferentiable minimization, J. Optim. Theory Appl, vol.109, issue.3, p.91, 2001. ,
Block-coordinate gradient descent method for linearly constrained nonsmooth separable optimization, J. Optim. Theory Appl, vol.140, issue.3, p.92, 2009. ,
Global optimization in the localization of neuromagnetic sources, IEEE Trans. Med. Imag, vol.45, issue.6, p.35, 1998. ,
Visualization of magnetoencephalographic data using minimum current estimates, NeuroImage, vol.10, issue.2, p.36, 1999. ,
Local smoothness in variance reduced optimization, NeurIPS, p.44, 2015. ,
Model consistency of partly smooth regularizers, IEEE Trans. Inf. Theory, vol.64, issue.3, p.74, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-00987293
Lecture notes from the 45th Probability Summer School held in Saint-Four, Lecture Notes in Mathematics, vol.2159, p.101, 2015. ,
? 2 -confidence sets in high-dimensional regression, Statistical analysis for high-dimensional data, p.97, 2016. ,
Localization of brain electrical activity via linearly constrained minimum variance spatial filtering, IEEE Transactions on biomedical engineering, vol.44, issue.9, p.35, 1997. ,
Bridge estimators and the adaptive Lasso under heteroscedasticity, Math. Methods Statist, vol.21, p.86, 2012. ,
Lasso screening rules via dual polytope projection, NeurIPS, vol.44, p.66, 2013. ,
A unified Bayesian framework for MEG/EEG source imaging, NeuroImage, vol.44, issue.3, p.36, 2009. ,
Estimating the location and orientation of complex, correlated neural activity using MEG, NeurIPS, vol.36, p.130, 2008. ,
On certain fundamental principles of scientific inquiry. The London, Edinburgh, and Dublin Philosophical Magazine, Journal of Science, vol.42, issue.249, p.22, 1921. ,
Coordinate descent algorithms for lasso penalized regression, Ann. Appl. Stat, p.24, 2008. ,
Fast lasso screening tests based on correlations, ICASSP, p.44, 2012. ,
Screening tests for lasso problems, IEEE Trans. Pattern Anal. Mach. Intell, pp.2016-66 ,
An improved GLMNET for l1-regularized logistic regression, J. Mach. Learn. Res, vol.13, p.78, 1999. ,
Model selection and estimation in regression with grouped variables, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.68, issue.1, p.66, 2006. ,
Nearly unbiased variable selection under minimax concave penalty, Ann. Statist, vol.38, issue.2, p.24, 2010. ,
Adaptive forward-backward greedy algorithm for learning sparse representations, IEEE Trans. Inf. Theory, vol.57, issue.7, p.23, 2011. ,
On model selection consistency of Lasso, J. Mach. Learn. Res, vol.7, p.24, 2006. ,
Blind source separation by sparse decomposition in a signal dictionary, Neural computation, vol.13, issue.4, p.26, 2001. ,
The adaptive lasso and its oracle properties, J. Amer. Statist. Assoc, vol.101, issue.476, p.25, 2006. ,
Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.67, issue.2, p.24, 2005. ,