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Risk estimation is at the core of many learning systems.
Robust regression: asymptotics, conjectures and monte carlo
Peter J. Huber · 1973
Earlier work this paper cites.
Some comments on c p c_{p}
C. Mallows · 1973
Earlier work this paper cites.
Cross-validatory choice and assesment of statistical predictions
Mervyn Stone · 1974
Earlier work this paper cites.
The relationship between variable selection and data augmentation and a method for prediction
David M. Allen · 1974
Earlier work this paper cites.
A new look at the statistical model identification
H. Akaike · 1974
Earlier work this paper cites.
The predictive sample reuse method with applications
Seymour Geisser · 1975
Earlier work this paper cites.
An asymptotic equivalence of choice of model by cross-validation and akaike’s criterion
Mervyn Stone · 1977
Earlier work this paper cites.
Estimating the correct degree of smoothing by the method of generalized cross-validation
Peter Craven and Grace Wahba · 1979
Earlier work this paper cites.
Generalized cross-validation as a method for choosing a good ridge parameter
Gene H. Golub, Michael Heath, and Grace Wahba · 1979
Earlier work this paper cites.
Estimation of the mean of a multivariate normal distribution
C. Stein · 1981
Earlier work this paper cites.
Estimating the error rate of a prediction rule: Improvement on cross-validation
Bradley Efron · 1983
Earlier work this paper cites.
How biased is the apparent error rate of a prediction rule?
B. Efron · 1986
Earlier work this paper cites.
Regression and time series model selection in small samples
C.M. Hurvich and C.L. Tsai · 1989
Earlier work this paper cites.
On the distribution of the largest eigenvalue in principal components analysis
Iain M Johnstone · 2001
Earlier work this paper cites.
The complex wishart distribution and the symmetric group
Piotr Graczyk, Gérard Letac, and Hélène Massam · 2003
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Yu Nesterov · 2005
Earlier work this paper cites.
Condition numbers of gaussian random matrices
Zizhong Chen and Jack J. Dongarra · 2005
Earlier work this paper cites.
Consistency of cross validation for comparing regression procedures
Y. Yang · 2007
Earlier work this paper cites.
Fast optimization methods for l1 regularization: A comparative study and two new approaches
Mark Schmidt, Glenn Fung, and Rmer Rosales · 2007
Earlier work this paper cites.
Message-passing algorithms for compressed sensing
David L. Donoho, Arian Maleki, and Andrea Montanari · 2009
Earlier work this paper cites.
Regularization parameter selections via generalized information criterion
Y. Zhang, R. Li, and C.L. Tsai · 2010
Earlier work this paper cites.
Message passing algorithms for compressed sensing: I. motivation and construction
David L Donoho, Arian Maleki, and Andrea Montanari · 2010
Cited alongside, same era.
Approximate message passing algorithm for compressed sensing
Arian Maleki · 2011
Cited alongside, same era.
Templates for convex cone problems with applications to sparse signal recovery
Stephen R Becker, Emmanuel J Candès, and Michael C Grant · 2011
Cited alongside, same era.
Generalized approximate message passing for estimation with random linear mixing
Sundeep Rangan · 2011
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The lasso risk for gaussian matrices
Mohsen Bayati and Andrea Montanari · 2012
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The convex geometry of linear inverse problems
Venkat Chandrasekaran, Benjamin Recht, Pablo A Parrilo, and Alan S Willsky · 2012
Cited alongside, same era.
Geometric inference for general high-dimensional linear inverse problems
T Tony Cai, Tengyuan Liang, Alexander Rakhlin, et al · 2016
Later among the works it cites.
From denoising to compressed sensing
Christopher A. Metzler, Arian Maleki, and Richard G. Baraniuk · 2016
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On optimal generalizability in parametric learning
Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, and Vahid Tarokh · 2017
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Pragya Sur, Yuxin Chen, and Emmanuel J. Candès · 2017
Later among the works it cites.
Risk consistency of cross-validation with lasso-type procedures
Homrighausen D and D.J. McDonald · 2017
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