mlsanityData to 7 Oct 2026

Generalized Linear Rule Models

arXiv 1906.01761 ↗First posted 5 Jun 2019cs.LG math.OC stat.MLcited by 34

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Abstract (arXiv)

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set complexity and prediction accuracy. Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one. The column generation subproblem is solved using either integer programming or a heuristic optimizing the same objective. In experiments involving logistic and linear regression, the proposed methods obtain better accuracy-complexity trade-offs than existing rule ensemble algorithms. At one end of the trade-off, the methods are competitive with less interpretable benchmark models.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2019.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 10; 2024: 6; 2025: 5; 2026: 2.

34 papers.

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  1. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  2. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  3. 2021Scalable Rule-Based Representation Learning for Interpretable Classificationcited by 40
  4. 2023Learning Interpretable Rules for Scalable Data Representation and Classificationcited by 14
  5. 2023Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learningcited by 11
  6. 2021Interpretable and Fair Boolean Rule Sets via Column Generationcited by 10
  7. 2021AI Explainability 360: Impact and Designcited by 8
  8. 2022On the Safety of Interpretable Machine Learning: A Maximum Deviation Approachcited by 7
  9. 2022Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Modelscited by 5
  10. 2023FIRE: An Optimization Approach for Fast Interpretable Rule Extractioncited by 5
  11. 2024Learning Cut Generating Functions for Integer Programmingcited by 5
  12. 2021Rule Generation for Classification: Scalability, Interpretability, and Fairnesscited by 4
22 more
  1. 2022A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tablescited by 4
  2. 2023TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecturecited by 4
  3. 2021Feature Cross Search via Submodular Optimizationcited by 3
  4. 2023MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Valuescited by 3
  5. 2024Distilled Datamodel with Reverse Gradient Matchingcited by 3
  6. 2024Orthogonal Gradient Boosting for Simpler Additive Rule Ensemblescited by 2
  7. 2024Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanationscited by 2
  8. 2025Compact Rule-Based Classifier Learning via Gradient Descentcited by 2
  9. 2025Bias Detection via Maximum Subgroup Discrepancycited by 2
  10. 2024A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programmingcited by 1
  11. 2022Deep Explainable Learning with Graph Based Data Assessing and Rule Reasoningcited by 0
  12. 2023Personalized Interpretable Classificationcited by 0
  13. 2023Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanationscited by 0
  14. 2023Learning Locally Interpretable Rule Ensemblecited by 0
  15. 2023Reinforcement Logic Rule Learning for Temporal Point Processescited by 0
  16. 2023A New Interpretable Neural Network-Based Rule Model for Healthcare Decision Makingcited by 0
  17. 2024The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizationscited by 0
  18. 2025MOSS: Multi-Objective Optimization for Stable Rule Setscited by 0
  19. 2025Interpretable Representation Learning for Additive Rule Ensemblescited by 0
  20. 2025Augmenting Intelligence: A Hybrid Framework for Scalable and Stable Explanationscited by 0
  21. 2026TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tablescited by 0
  22. 2026FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Predictioncited by 0