Sharp Time–Data Tradeoffs for Linear Inverse Problems
暂无分享,去创建一个
[1] J. Claerbout,et al. Robust Modeling With Erratic Data , 1973 .
[2] J. Högbom,et al. APERTURE SYNTHESIS WITH A NON-REGULAR DISTRIBUTION OF INTERFEROMETER BASELINES. Commentary , 1974 .
[3] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[4] Y. Gordon. On Milman's inequality and random subspaces which escape through a mesh in ℝ n , 1988 .
[5] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[6] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[7] M. Talagrand. The Generic Chaining , 2005 .
[8] M. Talagrand. The Generic chaining : upper and lower bounds of stochastic processes , 2005 .
[9] Wotao Yin,et al. An Iterative Regularization Method for Total Variation-Based Image Restoration , 2005, Multiscale Model. Simul..
[10] D. Donoho,et al. Neighborliness of randomly projected simplices in high dimensions. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[11] D. Donoho,et al. Sparse nonnegative solution of underdetermined linear equations by linear programming. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[12] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[13] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[14] M. Rudelson,et al. Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements , 2006, 2006 40th Annual Conference on Information Sciences and Systems.
[15] David L. Donoho,et al. High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension , 2006, Discret. Comput. Geom..
[16] D. Donoho,et al. Thresholds for the Recovery of Sparse Solutions via L1 Minimization , 2006, 2006 40th Annual Conference on Information Sciences and Systems.
[17] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .
[18] Mário A. T. Figueiredo,et al. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.
[19] Alessandro Foi,et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.
[20] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[21] S. Mendelson,et al. Reconstruction and Subgaussian Operators in Asymptotic Geometric Analysis , 2007 .
[22] Wotao Yin,et al. Bregman Iterative Algorithms for (cid:2) 1 -Minimization with Applications to Compressed Sensing ∗ , 2008 .
[23] Mike E. Davies,et al. Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.
[24] Rayan Saab,et al. Stable sparse approximations via nonconvex optimization , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[25] Wotao Yin,et al. Iteratively reweighted algorithms for compressive sensing , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[26] David L. Donoho,et al. Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[27] Jieping Ye,et al. An accelerated gradient method for trace norm minimization , 2009, ICML '09.
[28] Mihailo Stojnic,et al. Various thresholds for ℓ1-optimization in compressed sensing , 2009, ArXiv.
[29] VershyninRoman,et al. Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit , 2009 .
[30] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[31] M. Stojnic. Various thresholds for $\ell_1$-optimization in compressed sensing , 2009 .
[32] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[33] Martin J. Wainwright,et al. Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional and Noisy Setting , 2007, IEEE Transactions on Information Theory.
[34] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[35] Andrea Montanari,et al. Matrix Completion from Noisy Entries , 2009, J. Mach. Learn. Res..
[36] Rahul Garg,et al. Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property , 2009, ICML '09.
[37] H. Rauhut. Compressive Sensing and Structured Random Matrices , 2009 .
[38] Deanna Needell,et al. Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit , 2007, Found. Comput. Math..
[39] Emmanuel J. Candès,et al. A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..
[40] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[41] Holger Rauhut,et al. The Gelfand widths of lp-balls for 0p<=1 , 2010, J. Complex..
[42] Inderjit S. Dhillon,et al. Guaranteed Rank Minimization via Singular Value Projection , 2009, NIPS.
[43] Andrea Montanari,et al. The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, 2010 IEEE International Symposium on Information Theory.
[44] Pablo A. Parrilo,et al. Latent variable graphical model selection via convex optimization , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[45] Holger Rauhut,et al. The Gelfand widths of ℓp-balls for 0 , 2010, ArXiv.
[46] Massimo Fornasier,et al. Theoretical Foundations and Numerical Methods for Sparse Recovery , 2010, Radon Series on Computational and Applied Mathematics.
[47] Robert Tibshirani,et al. Spectral Regularization Algorithms for Learning Large Incomplete Matrices , 2010, J. Mach. Learn. Res..
[48] Martin J. Wainwright,et al. Fast global convergence rates of gradient methods for high-dimensional statistical recovery , 2010, NIPS.
[49] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[50] Sundeep Rangan,et al. Generalized approximate message passing for estimation with random linear mixing , 2010, 2011 IEEE International Symposium on Information Theory Proceedings.
[51] Emmanuel J. Candès,et al. A Probabilistic and RIPless Theory of Compressed Sensing , 2010, IEEE Transactions on Information Theory.
[52] David Gross,et al. Recovering Low-Rank Matrices From Few Coefficients in Any Basis , 2009, IEEE Transactions on Information Theory.
[53] Emmanuel J. Candès,et al. NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..
[54] Weiyu Xu,et al. Null space conditions and thresholds for rank minimization , 2011, Math. Program..
[55] Bhiksha Raj,et al. A unifying analysis of projected gradient descent for ℓp-constrained least squares , 2011, 1107.4623.
[56] Babak Hassibi,et al. Tight recovery thresholds and robustness analysis for nuclear norm minimization , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.
[57] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[58] Andrea Montanari,et al. The LASSO Risk for Gaussian Matrices , 2010, IEEE Transactions on Information Theory.
[59] Andrea Montanari,et al. Universality in Polytope Phase Transitions and Message Passing Algorithms , 2012, ArXiv.
[60] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[61] Alekh Agarwal,et al. Computational Trade-offs in Statistical Learning , 2012 .
[62] Holger Rauhut,et al. Suprema of Chaos Processes and the Restricted Isometry Property , 2012, ArXiv.
[63] Zongben Xu,et al. $L_{1/2}$ Regularization: A Thresholding Representation Theory and a Fast Solver , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[64] Po-Ling Loh,et al. Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima , 2013, J. Mach. Learn. Res..
[65] Michael I. Jordan,et al. Computational and statistical tradeoffs via convex relaxation , 2012, Proceedings of the National Academy of Sciences.
[66] Venkatesan Guruswami,et al. Restricted Isometry of Fourier Matrices and List Decodability of Random Linear Codes , 2012, SIAM J. Comput..
[67] Christos Thrampoulidis,et al. The squared-error of generalized LASSO: A precise analysis , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[68] Mihailo Stojnic. Upper-bounding ℓ1-optimization weak thresholds , 2013, ArXiv.
[69] Lin Xiao,et al. A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem , 2012, SIAM J. Optim..
[70] Xiaodong Li,et al. Phase Retrieval from Coded Diffraction Patterns , 2013, 1310.3240.
[71] Mihailo Stojnic,et al. A framework to characterize performance of LASSO algorithms , 2013, ArXiv.
[72] S. Foucart,et al. Random Sampling in Bounded Orthonormal Systems , 2013 .
[73] Mihailo Stojnic,et al. A performance analysis framework for SOCP algorithms in noisy compressed sensing , 2013, ArXiv.
[74] Emmanuel J. Candès,et al. Simple bounds for recovering low-complexity models , 2011, Math. Program..
[75] M. Stojnic. Upper-bounding $\ell_1$-optimization weak thresholds , 2013 .
[76] Andrea Montanari,et al. Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising , 2011, IEEE Transactions on Information Theory.
[77] Volkan Cevher,et al. Fixed Points of Generalized Approximate Message Passing With Arbitrary Matrices , 2016, IEEE Transactions on Information Theory.
[78] Joel A. Tropp,et al. Living on the edge: phase transitions in convex programs with random data , 2013, 1303.6672.
[79] Christos Thrampoulidis,et al. Simple Bounds for Noisy Linear Inverse Problems with Exact Side Information , 2013, ArXiv.
[80] Tengyuan Liang,et al. Geometric Inference for General High-Dimensional Linear Inverse Problems , 2014, 1404.4408.
[81] Vidyashankar Sivakumar,et al. Estimation with Norm Regularization , 2014, NIPS.
[83] Y. Plan,et al. High-dimensional estimation with geometric constraints , 2014, 1404.3749.
[84] Prateek Jain,et al. On Iterative Hard Thresholding Methods for High-dimensional M-Estimation , 2014, NIPS.
[85] Volkan Cevher,et al. Time-Data Tradeoffs by Aggressive Smoothing , 2014, NIPS.
[86] T. T. Cai,et al. Geometrizing Local Rates of Convergence for Linear Inverse Problems , 2014 .
[87] R. Vershynin. Estimation in High Dimensions: A Geometric Perspective , 2014, 1405.5103.
[88] Shahar Mendelson,et al. Learning without Concentration , 2014, COLT.
[89] Ya-Ping Hsieh,et al. A Geometric View on Constrained M-Estimators , 2015, 1506.08163.
[90] Volkan Cevher,et al. Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost , 2015, IEEE Journal of Selected Topics in Signal Processing.
[91] Benjamin Recht,et al. Isometric sketching of any set via the Restricted Isometry Property , 2015, ArXiv.
[92] Joydeep Ghosh,et al. Unified View of Matrix Completion under General Structural Constraints , 2015, NIPS.
[93] Xiaodong Li,et al. Phase Retrieval via Wirtinger Flow: Theory and Algorithms , 2014, IEEE Transactions on Information Theory.
[94] S. Frick,et al. Compressed Sensing , 2014, Computer Vision, A Reference Guide.
[95] Joel A. Tropp,et al. Universality laws for randomized dimension reduction, with applications , 2015, ArXiv.
[96] Richard G. Baraniuk,et al. From Denoising to Compressed Sensing , 2014, IEEE Transactions on Information Theory.
[97] Sjoerd Dirksen,et al. Dimensionality Reduction with Subgaussian Matrices: A Unified Theory , 2014, Foundations of Computational Mathematics.
[98] Yaniv Plan,et al. The Generalized Lasso With Non-Linear Observations , 2015, IEEE Transactions on Information Theory.
[99] Babak Hassibi,et al. Asymptotically Exact Denoising in Relation to Compressed Sensing , 2013, ArXiv.
[100] Arindam Banerjee,et al. Structured Matrix Recovery via the Generalized Dantzig Selector , 2016, NIPS.
[101] Samet Oymak,et al. Fast and Reliable Parameter Estimation from Nonlinear Observations , 2016, SIAM J. Optim..
[102] Arian Maleki,et al. Does $\ell _{p}$ -Minimization Outperform $\ell _{1}$ -Minimization? , 2015, IEEE Transactions on Information Theory.
[103] Maryam Fazel,et al. Decomposable norm minimization with proximal-gradient homotopy algorithm , 2015, Comput. Optim. Appl..