To cite the sparse-group SLOPE method in publications use
Feser F, Evangelou M (2023).“Sparse-group SLOPE: adaptive bi-level selection with FDR-control.”arXiv.doi:10.48550/arXiv.2305.09467,https://arxiv.org/abs/2305.09467.
Feser F, Evangelou M (2025).“Strong Screening Rules for Group-based SLOPE Models.”In Li Y, Mandt S, Agrawal S, Khan E (eds.),Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, volume 258 series Proceedings of Machine Learning Research, 352–360.https://proceedings.mlr.press/v258/feser25a.html.
To cite the sgs R package in publications use:
Feser F (2023).sgs.https://CRAN.R-project.org/package=sgs.
Corresponding BibTeX entries:
@Article{, title = {Sparse-group SLOPE: adaptive bi-level selection with FDR-control}, author = {Fabio Feser and Marina Evangelou}, journal = {arXiv}, year = {2023}, doi = {10.48550/arXiv.2305.09467}, url = {https://arxiv.org/abs/2305.09467}, } @InProceedings{, title = {Strong Screening Rules for Group-based SLOPE Models}, author = {Fabio Feser and Marina Evangelou}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {352--360}, year = {2025}, editor = {Yingzhen Li and Stephan Mandt and Shipra Agrawal and Emtiyaz Khan}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/feser25a/feser25a.pdf}, url = {https://proceedings.mlr.press/v258/feser25a.html}, abstract = {Tuning the regularization parameter in penalized regression models is an expensive task, requiring multiple models to be fit along a path of parameters. Strong screening rules drastically reduce computational costs by lowering the dimensionality of the input prior to fitting. We develop strong screening rules for group-based Sorted L-One Penalized Estimation (SLOPE) models: Group SLOPE and Sparse-group SLOPE. The developed rules are applicable to the wider family of group-based OWL models, including OSCAR. Our experiments on both synthetic and real data show that the screening rules significantly accelerate the fitting process. The screening rules make it accessible for group SLOPE and sparse-group SLOPE to be applied to high-dimensional datasets, particularly those encountered in genetics.}, } @Manual{, title = {sgs}, author = {Fabio Feser}, year = {2023}, url = {https://CRAN.R-project.org/package=sgs}, }