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Ridge or more formally $\ell_2$ regularization shows up in many areas of statistics and machine learning.
Estimation with quadratic loss
W. James and Charles Stein · 1961
Earlier work this paper cites.
Ridge regression: biased estimation for nonorthogonal problems
A. E. Hoerl and R.W Kennard · 1970
Earlier work this paper cites.
Generalized cross-validation as a method for choosing a good ridge parameter
G. Golub, M. Heath, and G. Wahba · 1979
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
Matrix Rank Minimization with Applications
Maryam Fazel · 2002
Earlier work this paper cites.
Efficient quadratic regularization for expression arrays
Trevor Hastie and Rob Tibshirani · 2004
Earlier work this paper cites.
Maximum-margin matrix factorization
N. Srebro, J. Rennie, and T. Jaakkola · 2005
Cited alongside, same era.
Regression shrinkage and selection via the elastic net
Hui Zou and Trevor Hastie · 2005
Cited alongside, same era.
Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin · 2007
Cited alongside, same era.
The Elements of Statistical Learning: Prediction, Inference and Data Mining
T. Hastie, R. Tibshirani, and J. Friedman · 2009
Cited alongside, same era.
Group Lasso with overlap and graph Lasso
Laurent Jacob, Guillaume Obozinski, and Jean-Philippe Vert · 2009
Cited alongside, same era.
Dropout training as adaptive regularization
Stefan Wager, Sida Wang, and Percy S Liang · 2013
Cited alongside, same era.
Generalized Additive Model Selection
A. Chouldechova and T. Hastie · 2015
Later among the works it cites.
Learning interactions via hierarchical group-lasso regularization
Michael Lim and Trevor Hastie · 2015
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Computer Age Statistical Inference; Algorithms, Evidence and Data Science
Bradley Efron and Trevor Hastie · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Reconciling modern machine learning and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2018
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Deep Learning with R
Francois Chollet and J. J. Allaire · 2018
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Matrix completion and low-rank SVD via fast alternating least squares
Trevor Hastie, Rahul Mazumder, Jason Lee, and Reza Zadeh
Cited in the paper.
Statistical Learning with Sparsity: the Lasso and Generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright
Cited in the paper.
Surprises in high-dimensional ridgeless least squares interpolation, 2019
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J. Tibshirani · 2019
Later among the works it cites.