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Found 4 papers, 3 papers with code
Hybrid safe-strong rules for efficient optimization in lasso-type problems
However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently scale up to problems of this size.
The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R
Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets.
Marginal false discovery rates for penalized regression models
Penalized regression methods are an attractive tool for high-dimensional data analysis, but their widespread adoption has been hampered by the difficulty of applying inferential tools.
Statistics Theory Statistics Theory
Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
Penalized regression is an attractive framework for variable selection problems.
