Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity
暂无分享,去创建一个
[1] Pradeep Ravikumar,et al. Dirty Statistical Models , 2013, NIPS.
[2] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[3] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[4] Richard G. Baraniuk,et al. Compressive Sensing , 2008, Computer Vision, A Reference Guide.
[5] Po-Ling Loh,et al. Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima , 2013, J. Mach. Learn. Res..
[6] Pradeep Ravikumar,et al. Graphical models via univariate exponential family distributions , 2013, J. Mach. Learn. Res..
[7] Martin J. Wainwright,et al. Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.
[8] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[9] Po-Ling Loh,et al. Support recovery without incoherence: A case for nonconvex regularization , 2014, ArXiv.
[10] Emmanuel J. Candès,et al. The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.
[11] Martin J. Wainwright,et al. Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE , 2008, NIPS.
[12] Ali Jalali,et al. A Dirty Model for Multi-task Learning , 2010, NIPS.
[13] Sham M. Kakade,et al. Robust Matrix Decomposition With Sparse Corruptions , 2011, IEEE Transactions on Information Theory.
[14] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[15] Jieping Ye,et al. Multi-stage multi-task feature learning , 2012, J. Mach. Learn. Res..
[16] Jian Huang,et al. A Selective Review of Group Selection in High-Dimensional Models. , 2012, Statistical science : a review journal of the Institute of Mathematical Statistics.
[17] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[18] Pablo A. Parrilo,et al. Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..
[19] Martin J. Wainwright,et al. Estimation of (near) low-rank matrices with noise and high-dimensional scaling , 2009, ICML.
[20] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[21] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[22] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[23] Takashi Washio,et al. Learning a common substructure of multiple graphical Gaussian models , 2012, Neural Networks.
[24] J. Lafferty,et al. Sparse additive models , 2007, 0711.4555.
[25] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[26] Martin J. Wainwright,et al. Restricted Eigenvalue Properties for Correlated Gaussian Designs , 2010, J. Mach. Learn. Res..