3 citations · 3 across the 3 of their papers we have counts for
4 papers
Coordinate Descent for SLOPE
Johan Larsson, Quentin Klopfenstein, Mathurin Massias +1
The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved.…
Look-Ahead Screening Rules for the Lasso
Johan Larsson
The lasso is a popular method to induce shrinkage and sparsity in the solution vector (coefficients) of regression problems, particularly when there are many predictors relative to…
The Hessian Screening Rule
Johan Larsson, Jonas Wallin
Predictor screening rules, which discard predictors before fitting a model, have had considerable impact on the speed with which sparse regression problems, such as the lasso, can…
The Strong Screening Rule for SLOPE
Johan Larsson, Małgorzata Bogdan, Jonas Wallin
Extracting relevant features from data sets where the number of observations () is much smaller then the number of predictors () is a major challenge in modern statistics. So…