Regression shrinkage and selection via the lasso: a retrospective

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Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, positive): “A LASSO penalty (Tibshirani, 1996) is used to reduce overfitting; the amount of penalization is chosen by minimizing 10-fold cross-validated prediction error.”
Analysis of nonsmooth stochastic approximation: the differential inclusion approach (2018, neutral): “In many Machine Learning applications f is the empirical risk of the model, and g a sparsity inducing penalties like LASSO [39], MCP [41], or SCAD [20].”
Choosing prediction over explanation in psychology: Lessons from machine learning (2017, neutral): “In a widely used form of penalized regression called lasso regression (Tibshirani, 1996, 2011), this least-squares criterion is retained, but the overall cost function that the estimation seeks to minimize now includes an additional penalty term that is proportional to the sum of the absolute…”
Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features (2013, neutral): “[27] ——, “Regression shrinkage and selection via the lasso: a retrospective,” J.”