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This paper studies M-estimators with gradient-Lipschitz loss function regularized with convex penalty in linear models with Gaussian design matrix and arbitrary noise distribution.
Distribution of eigenvalues for some sets of random matrices
Vladimir A Marčenko and Leonid Andreevich Pastur · 1967
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Estimation of the mean of a multivariate normal distribution
Charles M Stein · 1981
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Weakly differentiable functions: Sobolev spaces and functions of bounded variation , volume 120
William P Ziemer · 1989
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Local operator theory, random matrices and banach spaces
Kenneth R Davidson and Stanislaw J Szarek · 2001
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Out-of-sample error estimate for robust m-estimators with convex penalty
Pierre C Bellec · 2008
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Fluctuations of eigenvalues and second order poincaré inequalities
Sourav Chatterjee · 2009
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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The lasso risk for gaussian matrices
Mohsen Bayati and Andrea Montanari · 2012
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Estimating lasso risk and noise level
Mohsen Bayati, Murat A Erdogdu, and Andrea Montanari · 2013
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Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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On robust regression with high-dimensional predictors
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High-dimensional asymptotics of prediction: Ridge regression and classification
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions
Kamiar Rahnama Rad, Wenda Zhou, and Arian Maleki · 2020
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Edgar Dobriban and Stefan Wager · 2018
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Precise error analysis of regularized m m -estimators in high dimensions
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2018
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Kamiar Rahnama Rad and Arian Maleki · 2020
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