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BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning

arXiv 2311.13695 ↗First posted 22 Nov 2023cs.LG math.OC stat.MLcited by 0

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Abstract (arXiv)

We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems. This optimization paradigm can naturally be used to formulate fundamental problems in interpretable supervised learning (e.g., sparse regression and decision trees), in unsupervised learning (e.g., clustering), and beyond; BackboneLearn solves the aforementioned problems faster than exact methods and with higher accuracy than commonly used heuristics. The package is built in Python and is user-friendly and easily extensible: users can directly implement a backbone algorithm for their MIO problem at hand. The source code of BackboneLearn is available on GitHub (link: https://github.com/chziakas/backbone_learn).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 22 Nov 2023.

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References · 12

  1. 2023Finding Neurons in a Haystack: Case Studies with Sparse Probingcited by 334
  2. 2020Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimizationcited by 45
  3. 2022Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimizationcited by 29
  4. 2022Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Featurescited by 20
  5. 2022L0Learn: A Scalable Package for Sparse Learning using L0 Regularizationcited by 12
  6. 2020Safe Screening Rules for ℓ0-Regressioncited by 7
  7. 2020The Backbone Method for Ultra-High Dimensional Sparse Machine Learningcited by 7
  8. 2021Slowly Varying Regression under Sparsitycited by 7
  9. 2022Safe Screening for Logistic Regression with ℓ0-ℓ2 Regularizationcited by 6
  10. 2022Sparse PCA With Multiple Componentscited by 4
  11. 2023Compressed Sensing: A Discrete Optimization Approachcited by 3
  12. 2023ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescriptioncited by 2

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