Discussion on: Sparse regression: Scalable algorithms and empirical performance & Best Subset, Forward Stepwise, or Lasso? Analysis and recommendations based on extensive comparisons
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[1] Trevor Hastie,et al. Statistical Learning with Sparsity: The Lasso and Generalizations , 2015 .
[2] Dimitris Bertsimas,et al. Algorithm for cardinality-constrained quadratic optimization , 2009, Comput. Optim. Appl..
[3] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[4] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[5] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[6] L. Breiman. Heuristics of instability and stabilization in model selection , 1996 .
[7] Lie Wang,et al. Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.
[8] Peter Buhlmann. Boosting for high-dimensional linear models , 2006, math/0606789.
[9] P. Massart,et al. Risk bounds for model selection via penalization , 1999 .
[10] 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.
[11] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[12] N. Meinshausen,et al. Spectral Deconfounding via Perturbed Sparse Linear Models , 2018, 1811.05352.
[13] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[14] L. Breiman. Better subset regression using the nonnegative garrote , 1995 .
[15] Marc Hofmann,et al. Efficient algorithms for computing the best subset regression models for large-scale problems , 2007, Comput. Stat. Data Anal..
[16] Bart P. G. Van Parys,et al. Sparse high-dimensional regression: Exact scalable algorithms and phase transitions , 2017, The Annals of Statistics.
[17] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[18] Dimitris Bertsimas,et al. Characterization of the equivalence of robustification and regularization in linear and matrix regression , 2017, Eur. J. Oper. Res..
[19] Renato D. C. Monteiro,et al. A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization , 2003, Math. Program..
[20] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[21] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[22] Shie Mannor,et al. Robust Regression and Lasso , 2008, IEEE Transactions on Information Theory.
[23] Liam Paninski,et al. Fast online deconvolution of calcium imaging data , 2016, PLoS Comput. Biol..
[24] D. Bertsimas,et al. Best Subset Selection via a Modern Optimization Lens , 2015, 1507.03133.
[25] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[26] Dimitris Bertsimas,et al. Logistic Regression: From Art to Science , 2017 .
[27] N. Meinshausen,et al. Anchor regression: Heterogeneous data meet causality , 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[28] Erricos John Kontoghiorghes,et al. A branch and bound algorithm for computing the best subset regression models , 2002 .
[29] Daniela Witten,et al. EXACT SPIKE TRAIN INFERENCE VIA ℓ0 OPTIMIZATION. , 2017, The annals of applied statistics.
[30] David Maxwell Chickering,et al. Optimal Structure Identification With Greedy Search , 2002, J. Mach. Learn. Res..
[31] Tong Zhang,et al. Sparse Recovery With Orthogonal Matching Pursuit Under RIP , 2010, IEEE Transactions on Information Theory.
[32] D. Donoho,et al. Basis pursuit , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.
[33] Prateek Jain,et al. On Iterative Hard Thresholding Methods for High-dimensional M-Estimation , 2014, NIPS.
[34] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[35] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[36] Jean-Jacques Fuchs,et al. On sparse representations in arbitrary redundant bases , 2004, IEEE Transactions on Information Theory.
[37] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[38] Yudong Chen,et al. Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation: Recent Theory and Fast Algorithms via Convex and Nonconvex Optimization , 2018, IEEE Signal Processing Magazine.
[39] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[40] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[41] Dimitris Bertsimas,et al. Certifiably Optimal Low Rank Factor Analysis , 2016, J. Mach. Learn. Res..
[42] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[43] Nicolai Meinshausen,et al. Relaxed Lasso , 2007, Comput. Stat. Data Anal..
[44] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[45] Sara van de Geer,et al. Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .
[46] S. Geer,et al. Statistics for big data: A perspective , 2018 .
[47] A. Atkinson. Subset Selection in Regression , 1992 .