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Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models.
Portfolio selection
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Absolute and monotonic norms
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Hierarchical clustering schemes
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Some results on Tchebycheffian spline functions
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Applied Linear Regression
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Augmented Lagrangian and operator-splitting methods in nonlinear mechanics
R. Glowinski and P. Le Tallec · 1989
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A linear-time median-finding algorithm for projecting a vector on the simplex of
N. Maculan and G. Galdino de Paula · 1989
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Applications of a splitting algorithm to decomposition in convex programming and variational inequalities
P. Tseng · 1991
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Nonlinear total variation based noise removal algorithms
L.I. Rudin, S. Osher, and E. Fatemi · 1992
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Matching pursuit in a time-frequency dictionary
S. Mallat and Z. Zhang · 1993
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Adapting to unknown smoothness via wavelet shrinkage
D.L. Donoho and I.M. Johnstone · 1995
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Sparse approximate solutions to linear systems
B.K. Natarajan · 1995
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Bayesian learning for neural networks, volume 118
R.M. Neal · 1996
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
B.A. Olshausen and D.J. Field · 1996
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Regression shrinkage and selection via the Lasso
R. Tibshirani · 1996
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Convergence rates in forward-backward splitting
G.H.G. Chen and R.T. Rockafellar · 1997
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Convex analysis
R.T. Rockafellar · 1997
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Online algorithms and stochastic approximations
L. Bottou · 1998
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Penalized regressions: the bridge versus the lasso
W.J. Fu · 1998
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Nonlinear programming
D.P. Bertsekas · 1999
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Atomic decomposition by basis pursuit
S.S. Chen, D.L. Donoho, and M.A. Saunders · 1999
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Forward sequential algorithms for best basis selection
S.F. Cotter, J. Adler, B. Rao, and K. Kreutz-Delgado · 1999
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Method of optimal directions for frame design
K. Engan, S.O. Aase, H. Husoy, et al · 1999
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Outcomes of the equivalence of adaptive ridge with least absolute shrinkage
Y. Grandvalet and S. Canu · 1999
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M.R. Osborne, B. Presnell, and B.A. Turlach · 2000
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Variable selection via nonconcave penalized likelihood and its oracle properties
J. Fan and R. Li · 2001
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A rank minimization heuristic with application to minimum order system approximation
M. Fazel, H. Hindi, and S. Boyd · 2001
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Greedy function approximation: a gradient boosting machine
J. H. Friedman · 2001
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Learning with Kernels
B. Schölkopf and A.J. Smola · 2001
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A simple and efficient algorithm for gene selection using sparse logistic regression
S.K. Shevade and S.S. Keerthi · 2003
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Multiple kernel learning, conic duality, and the SMO algorithm
F. Bach, G. R. G. Lanckriet, and M. I. Jordan · 2004
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Large scale online learning
L. Bottou and Y. LeCun · 2004
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Convex Optimization
S. Boyd and L. Vandenberghe · 2004
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Least angle regression
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani · 2004
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A statistical framework for genomic data fusion
G. R. G. Lanckriet, T. De Bie, N. Cristianini, M. I. Jordan, and W. S. Noble · 2004
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Learning the kernel matrix with semidefinite programming
G.R.G. Lanckriet, N. Cristianini, L. El Ghaoui, P. Bartlett, and M.I. Jordan · 2004
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Introductory lectures on convex optimization: a basic course
Y. Nesterov · 2004
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Kernel Methods for Pattern Analysis
J. Shawe-Taylor and N. Cristianini · 2004
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Greed is good: Algorithmic results for sparse approximation
J.A. Tropp · 2004
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Sparse bayesian learning for basis selection
D.P. Wipf and B.D. Rao · 2004
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Total variation minimization and a class of binary MRF models
A. Chambolle · 2005
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Spike and slab variable selection: frequentist and Bayesian strategies
H. Ishwaran and J.S. Rao · 2005
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Sparse multinomial logistic regression: Fast algorithms and generalization bounds
B. Krishnapuram, L. Carin, M.A.T. Figueiredo, and A.J. Hartemink · 2005
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Smooth minimization of non-smooth functions
Y. Nesterov · 2005
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Maximum-margin matrix factorization
N. Srebro, J.D.M. Rennie, and T.S. Jaakkola · 2005
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Sparsity and smoothness via the fused Lasso
R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight · 2005
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Simultaneous variable selection
B.A. Turlach, W.N. Venables, and S.J. Wright · 2005
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Regularization and variable selection via the elastic net
H. Zou and T. Hastie · 2005
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K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation
M. Aharon, M. Elad, and A. Bruckstein · 2006
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Convex analysis and nonlinear optimization: theory and examples
J.M. Borwein and A.S. Lewis · 2006
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Signal recovery by proximal forward-backward splitting
P.L. Combettes and V.R. Wajs · 2006
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Image denoising via sparse and redundant representations over learned dictionaries
M. Elad and M. Aharon · 2006
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Sparse approximation via iterative thresholding
K.K. Herrity, A.C. Gilbert, and J.A. Tropp · 2006
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Numerical Optimization
J. Nocedal and S.J. Wright · 2006
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Large scale multiple kernel learning
S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf · 2006
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Sparse additive models
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman · 2009
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Stochastic methods for
S. Shalev-Shwartz and A. Tewari · 2009
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Lectures on stochastic programming: modeling and theory
A. Shapiro, D. Dentcheva, and A.P. Ruszczyński · 2009
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A coordinate gradient descent method for nonsmooth separable minimization
P. Tseng and S. Yun · 2009
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Sharp thresholds for noisy and high-dimensional recovery of sparsity using
M.J. Wainwright · 2009
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Robust face recognition via sparse representation
J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma · 2009
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Algorithms for simultaneous sparse approximation. part I: Greedy pursuit
J.A. Tropp, A.C. Gilbert, and M.J. Strauss · 2006
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Model selection and estimation in regression with grouped variables
M. Yuan and Y. Lin · 2006
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On model selection consistency of Lasso
P. Zhao and B. Yu · 2006
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Uncovering shared structures in multiclass classification
Y. Amit, M. Fink, N. Srebro, and S. Ullman · 2007
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Large-scale bayesian logistic regression for text categorization
A. Genkin, D.D Lewis, and D. Madigan · 2007
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An interior-point method for large-scale l1-regularized logistic regression
K. Koh, S.J. Kim, and S. Boyd · 2007
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Sparse reconstruction by separable approximation
S.J. Wright, R.D. Nowak, and M.A.T. Figueiredo · 2009
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The composite absolute penalties family for grouped and hierarchical variable selection
P. Zhao, G. Rocha, and B. Yu · 2009
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Structured sparsity-inducing norms through submodular functions
F. Bach · 2010
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Model-based compressive sensing
R.G. Baraniuk, V. Cevher, M. Duarte, and C. Hegde · 2010
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A singular value thresholding algorithm for matrix completion
J.F. Cai, E.J. Candès, and Z. Shen · 2010
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The convex geometry of linear inverse problems
V. Chandrasekaran, B. Recht, P.A. Parrilo, and A.S. Willsky · 2010
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Iteratively reweighted least squares minimization for sparse recovery
I. Daubechies, R. DeVore, M. Fornasier, and C. S. Güntürk · 2010
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A note on the group lasso and a sparse group lasso
J. Friedman, T. Hastie, and R. Tibshirani · 2010
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The benefit of group sparsity
J. Huang and T. Zhang · 2010
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Proximal methods for sparse hierarchical dictionary learning
R. Jenatton, J. Mairal, G. Obozinski, and F. Bach · 2010
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Structured sparse principal component analysis
R. Jenatton, G. Obozinski, and F. Bach · 2010
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Tree-guided group lasso for multi-task regression with structured sparsity
S. Kim and E.P. Xing · 2010
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Sparse coding for machine learning, image processing and computer vision
J. Mairal · 2010
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Online learning for matrix factorization and sparse coding
J. Mairal, F. Bach, J. Ponce, and G. Sapiro · 2010
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Network flow algorithms for structured sparsity
J. Mairal, R. Jenatton, G. Obozinski, and F. Bach · 2010
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S. Mosci, L. Rosasco, M. Santoro, A. Verri, and S. Villa · 2010
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Efficiency of coordinate descent methods on huge-scale optimization problems
Y. Nesterov · 2010
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Convex structure learning in log-linear models: Beyond pairwise potentials
M. Schmidt and K. Murphy · 2010
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Sparse structured dictionary learning for brain resting-state activity modeling
G. Varoquaux, R. Jenatton, A. Gramfort, G. Obozinski, B. Thirion, and F. Bach · 2010
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Fast detection of multiple change-points shared by many signals using group LARS
J.-P. Vert and K. Bleakley · 2010
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Accelerated block-coordinate relaxation for regularized optimization
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Dual averaging methods for regularized stochastic learning and online optimization
L. Xiao · 2010
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Robust PCA via outlier pursuit
H. Xu, C. Caramanis, and S. Sanghavi · 2010
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A comparison of optimization methods for large-scale l1-regularized linear classification
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Variable sparsity kernel learning
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