mlsanityData to 14 Oct 2026

L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

arXiv 2202.04820 ↗First posted 10 Feb 2022cs.LG cs.MS stat.COcited by 12

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Abstract (arXiv)

We present L0Learn: an open-source package for sparse linear regression and classification using ℓ0 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 (https://cran.r-project.org/package=L0Learn and https://github.com/hazimehh/L0Learn).

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

8 of 38 references.

References · 8

  1. 2015Best Subset Selection via a Modern Optimization Lenscited by 100
  2. 2018Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithmscited by 53
  3. 2020Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimizationcited by 45
  4. 2017Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitionscited by 43
  5. 2020Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectivescited by 26
  6. 2017Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is lowcited by 17
  7. 2021Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectivescited by 12
  8. 2020Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Pythoncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 5; 2026: 1.

12 papers.

  1. 2025Foundations of Top-k Decoding For Language Modelscited by 10
  2. 2024Interpretable Generalized Additive Models for Datasets with Missing Valuescited by 7
  3. 2023FIRE: An Optimization Approach for Fast Interpretable Rule Extractioncited by 5
  4. 2023Outlier detection in regression: conic quadratic formulationscited by 3
  5. 2023Efficient Cross-Validation for Sparse Linear Regressioncited by 0
  6. 2023BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learningcited by 0
  7. 2024FAST: An Optimization Framework for Fast Additive Segmentation in Transparent MLcited by 0
  8. 2025Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regressioncited by 0
  9. 2025A Generic Branch-and-Bound Algorithm for ℓ0-Penalized Problems with Supplementary Materialcited by 0
  10. 2025El0ps: An Exact L0-regularized Problems Solvercited by 0
  11. 2025MOSS: Multi-Objective Optimization for Stable Rule Setscited by 0
  12. 2026A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regressioncited by 0