mlsanityData to 14 Oct 2026

Strong Screening Rules for Group-based SLOPE Models

arXiv 2405.15357 ↗First posted 24 May 2024stat.ML cs.LG stat.MEcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 80 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

24 of 80 references.

References · 24

  1. 2016Gap Safe screening rules for sparsity enforcing penaltiescited by 39
  2. 2014SLOPE - Adaptive variable selection via convex optimizationcited by 33
  3. 2015Mind the duality gap: safer rules for the Lassocited by 33
  4. 2020Fast OSCAR and OWL Regression via Safe Screening Rulescited by 25
  5. 2016Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modelingcited by 18
  6. 2013A Safe Screening Rule for Sparse Logistic Regressioncited by 14
  7. 2016Group SLOPE - adaptive selection of groups of predictorscited by 13
  8. 2016GAP Safe Screening Rules for Sparse-Group-Lassocited by 10
  9. 2020The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimationcited by 9
  10. 2022sparsegl: An R Package for Estimating Sparse Group Lassocited by 9
  11. 2020The Strong Screening Rule for SLOPEcited by 8
  12. 2014Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Setscited by 7
12 more
  1. 2018Adaptive Three Operator Splittingcited by 7
  2. 2021Safe rules for the identification of zeros in the solutions of the SLOPE problemcited by 6
  3. 2023A Survey of Numerical Algorithms that can Solve the Lasso Problemscited by 5
  4. 2023Sparse-group SLOPE: adaptive bi-level selection with FDR-controlcited by 5
  5. 2022Coordinate Descent for SLOPEcited by 4
  6. 2014Sparse Estimation with Strongly Correlated Variables using Ordered Weighted L1 Regularizationcited by 3
  7. 2020An Exact Solution Path Algorithm for SLOPE and Quasi-Spherical OSCARcited by 3
  8. 2022Sparse Graphical Modelling via the Sorted L1-Normcited by 3
  9. 2017Hybrid safe-strong rules for efficient optimization in lasso-type problemscited by 2
  10. 2017ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Modelscited by 2
  11. 2016A study on tuning parameter selection for the high-dimensional lassocited by 1
  12. 2017High-dimensional robust regression and outliers detection with SLOPEcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variantcited by 0
  2. 2025Safe Screening Rules for Group SLOPEcited by 0