mlsanityData to 7 Oct 2026

Safe Screening for Logistic Regression with ℓ0-ℓ2 Regularization

arXiv 2202.00467 ↗First posted 1 Feb 2022stat.ML cs.LG stat.MEcited by 6

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Abstract (arXiv)

In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels. In this paper, we present screening rules that safely remove features from logistic regression with ℓ0-ℓ2 regularization before solving the problem. The proposed safe screening rules are based on lower bounds from the Fenchel dual of strong conic relaxations of the logistic regression problem. Numerical experiments with real and synthetic data suggest that a high percentage of the features can be effectively and safely removed apriori, leading to substantial speed-up in the computations.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 1 Feb 2022.

Builds on

10 of 29 references.

References · 10

  1. 2015Best Subset Selection via a Modern Optimization Lenscited by 99
  2. 2018Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithmscited by 52
  3. 2017Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitionscited by 43
  4. 2016Gap Safe screening rules for sparsity enforcing penaltiescited by 38
  5. 2015Mind the duality gap: safer rules for the Lassocited by 31
  6. 2020Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectivescited by 26
  7. 2013A Safe Screening Rule for Sparse Logistic Regressioncited by 14
  8. 2013Safe Screening With Variational Inequalities and Its Application to LASSOcited by 11
  9. 2018Sparse and Smooth Signal Estimation: Convexification of L0 Formulationscited by 10
  10. 2021Expanding boundaries of Gap Safe screeningcited by 7

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Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 1.

6 papers.

  1. 2024PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applicationscited by 5
  2. 2023Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspectivecited by 3
  3. 2023Constrained Optimization of Rank-One Functions with Indicator Variablescited by 2
  4. 2024Fair and Accurate Regression: Strong Formulations and Algorithmscited by 1
  5. 2023BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learningcited by 0
  6. 2025A Generic Branch-and-Bound Algorithm for ℓ0-Penalized Problems with Supplementary Materialcited by 0