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Found 5 papers, 4 papers with code
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Coordinate Descent for SLOPE
The lasso is the most famous sparse regression and feature selection method.
The Strong Screening Rule for SLOPE
We develop a screening rule for SLOPE by examining its subdifferential and show that this rule is a generalization of the strong rule for the lasso.
The Hessian Screening Rule
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 be solved.
Look-Ahead Screening Rules for the Lasso
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 number of observations.
