mlsanityData to 7 Oct 2026

abess: A Fast Best Subset Selection Library in Python and R

arXiv 2110.09697 ↗First posted 19 Oct 2021stat.ML cs.LG stat.COcited by 16

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Abstract (arXiv)

We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and ℓ2 regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index. In addition, a user-friendly R library is available at the Comprehensive R Archive Network. The source code is available at: https://github.com/abess-team/abess.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 7 of its 36 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

7 of 36 references.

References · 7

  1. 2019OpenML-Python: an extensible Python API for OpenMLcited by 62
  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. 2018Celer: a Fast Solver for the Lasso with Dual Extrapolationcited by 31
  5. 2017Accelerated Alternating Projections for Robust Principal Component Analysiscited by 25
  6. 2014Community detection in sparse networks via Grothendieck's inequalitycited by 21
  7. 2021A Splicing Approach to Best Subset of Groups Selectioncited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 5; 2025: 3; 2026: 2.

16 papers.

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  1. 2022Noise-aware Physics-informed Machine Learning for Robust PDE Discoverycited by 14
  2. 2024A sparse PAC-Bayesian approach for high-dimensional quantile predictioncited by 6
  3. 2025High-dimensional Bayesian Tobit regression for censored response with Horseshoe priorcited by 4
  4. 2023Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Techniquecited by 3
  5. 2022Holistic Generalized Linear Modelscited by 2
  6. 2026Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offscited by 2
  7. 2023On the Computational Complexity of Private High-dimensional Model Selectioncited by 1
  8. 2024skscope: Fast Sparsity-Constrained Optimization in Pythoncited by 1
  9. 2024FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Modelscited by 1
  10. 2023Understanding Best Subset Selection: A Tale of Two C(omplex)itiescited by 0
  11. 2023A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Modelscited by 0
  12. 2024IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insightcited by 0
4 more
  1. 2024Automated Model Selection for Generalized Linear Modelscited by 0
  2. 2025Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptationcited by 0
  3. 2025Exponential Lasso: robust sparse penalization under heavy-tailed noise and outliers with exponential-type losscited by 0
  4. 2026spca: An R package to Compute Least Squares Sparse Principal Componentscited by 0