L0Learn: A Scalable Package for Sparse Learning Using L0 Regularization
Abstract
We present L0Learn: an open-source package for sparse linear regression and classification using $\ell_0$ regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has user-friendly R and Python interfaces. L0Learn can address problems with millions of features, achieving competitive run times and statistical performance with state-of-the-art sparse learning packages. L0Learn is available on both CRAN and GitHub.
Cite
Text
Hazimeh et al. "L0Learn: A Scalable Package for Sparse Learning Using L0 Regularization." Machine Learning Open Source Software, 2023.Markdown
[Hazimeh et al. "L0Learn: A Scalable Package for Sparse Learning Using L0 Regularization." Machine Learning Open Source Software, 2023.](https://mlanthology.org/mloss/2023/hazimeh2023jmlr-l0learn/)BibTeX
@article{hazimeh2023jmlr-l0learn,
title = {{L0Learn: A Scalable Package for Sparse Learning Using L0 Regularization}},
author = {Hazimeh, Hussein and Mazumder, Rahul and Nonet, Tim},
journal = {Machine Learning Open Source Software},
year = {2023},
pages = {1-8},
volume = {24},
url = {https://mlanthology.org/mloss/2023/hazimeh2023jmlr-l0learn/}
}