Addendum: Regularization and variable selection via the elastic net

By Hui Zou, Trevor J. Hastie. 2005

10,689 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability, Optimization & Training

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Citations in the corpus

Stealing Hyperparameters in Machine Learning (2018, methodology): “We use an example ML algorithm, i.e., Elastic Net [58], with two hyperparameters to illustrate our attacks in Appendix B.”
Is Feature Selection Secure against Training Data Poisoning? (2015, neutral): “…this framework to formalize poisoning attack strategies against popular embedded feature selection methods, including the so-called least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996), ridge regression (Hoerl & Kennard, 1970), and the elastic net (Zou & Hastie, 2005) (Sect.”
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017, methodology): “In unshown experiments, we also tested elastic net regularization (Zou and Hastie 2005), a linear combination of L1 and L2 penalities.”
Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “However, LASSO and L1-regression based methods are designed to eliminate redundant features Zou and Hastie (2005) to a much greater extent than L2-regularized methods such as SVMs.”
Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, background): “It has become very popular with the advent of the LASSO (Tibshirani, 1996) and elastic net (Zou and Hastie, 2005).”
Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “Current methods for sparse linear classification such as the lasso (Tibshirani, 1996) and elastic net (Zou and Hastie, 2005) control the accuracy and sparsity of models via approximations to speed up computation, and require rounding to yield models with coprime integer coefficients.”
Supersparse Linear Integer Models for Predictive Scoring Systems (2013, methodology): “Current linear methods such as the lasso, elastic net and LARS are not designed to create scoring systems, since regularization is primarily used to improve accuracy as opposed to sparsity and interpretability (Tibshirani 1996; Zou and Hastie 2005; Efron et al. 2004).”
Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “In our analysis, we focus on the lasso (Chen et al., 1998; Tibshirani, 1996); however, in experiments, we use the elastic net for additional stability (Zou and Hastie, 2005).”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, background): “For example, the elastic net introduced by Zou and Hastie (2005) places the same set of covariates under both a LASSO and ridge constraint to help reduce mis-selections among correlated covariates.”
Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015, positive): “Ridge (Hoerl and Kennard 1970), Lasso (Tibshirani 1996) and Elastic net (Zou and Hastie 2005) are standard linear regression models with different regularizers.”
Variance-based Regularization with Convex Objectives (2016, background): “2  r , which is equivalent to elastic net regularization [27], while varying a 1 , a 2 , and r.”
Regularization Paths for Generalized Linear Models via Coordinate Descent (2010, background): “Many coeﬃcients are zero, remain zero after the thresholding, and so nothing needs to be changed.”
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples (2017, methodology): “Elastic-net regularization is a linear mixture of L1 and L2 penalty functions, and it has been a standard tool for high-dimensional feature selection problems (Zou and Hastie 2005).”
On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, methodology): “Notably, elastic net regularization (Zou & Hastie, 2005) as used in Carroll et al. (2009) is a hybrid of ℓ 1 -norm and ℓ 2 -norm regularization, and therefore delivers solutions which are in between LLR-L2 and LLR-L2 in terms of sparsity.”
An asynchronous mini-batch algorithm for regularized stochastic optimization (2015, neutral): “, [1]–[3]).”