mlsanityData to 14 Oct 2026

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models

arXiv 2303.16047 ↗First posted 28 Mar 2023cs.LG cs.AI stat.MLcited by 30

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Abstract (arXiv)

In real applications, interaction between machine learning models and domain experts is critical; however, the classical machine learning paradigm that usually produces only a single model does not facilitate such interaction. Approximating and exploring the Rashomon set, i.e., the set of all near-optimal models, addresses this practical challenge by providing the user with a searchable space containing a diverse set of models from which domain experts can choose. We present algorithms to efficiently and accurately approximate the Rashomon set of sparse, generalized additive models with ellipsoids for fixed support sets and use these ellipsoids to approximate Rashomon sets for many different support sets. The approximated Rashomon set serves as a cornerstone to solve practical challenges such as (1) studying the variable importance for the model class; (2) finding models under user-specified constraints (monotonicity, direct editing); and (3) investigating sudden changes in the shape functions. Experiments demonstrate the fidelity of the approximated Rashomon set and its effectiveness in solving practical challenges.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 28 Mar 2023.

Builds on

17 of 36 references.

References · 17

  1. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  2. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
  3. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 373
  4. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
  5. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 246
  6. 2017A Rewriting System for Convex Optimization Problemscited by 179
  7. 2019Predictive Multiplicity in Classificationcited by 166
  8. 2019On the Existence of Simpler Machine Learning Modelscited by 89
  9. 2022Exploring the Whole Rashomon Set of Sparse Decision Treescited by 81
  10. 2022Rashomon Capacity: A Metric for Predictive Multiplicity in Classificationcited by 47
  11. 2022Fast Sparse Classification for Generalized Linear and Additive Modelscited by 19
  12. 2021GAM Changer: Editing Generalized Additive Models with Interactive Visualizationcited by 18
5 more
  1. 2014Robust Optimization using Machine Learning for Uncertainty Setscited by 14
  2. 2018A Theory of Statistical Inference for Ensuring the Robustness of Scientific Resultscited by 13
  3. 2021Forward Stability and Model Path Selectioncited by 12
  4. 2021Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Setcited by 12
  5. 2019Generalized Sparse Additive Modelscited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 14; 2026: 8.

30 papers.

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  1. 2024Amazing Things Come From Having Many Good Modelscited by 55
  2. 2023A Path to Simpler Models Starts With Noisecited by 33
  3. 2024Predictive Churn with the Set of Good Modelscited by 15
  4. 2025Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Timecited by 13
  5. 2025Embracing Contradiction: Theoretical Inconsistency Will Not Impede the Road of Building Responsible AI Systemscited by 8
  6. 2025SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumerationcited by 6
  7. 2024X Hacking: The Threat of Misguided AutoMLcited by 5
  8. 2023Learning Diverse Features in Vision Transformers for Improved Generalizationcited by 4
  9. 2025Birds look like cars: Adversarial analysis of intrinsically interpretable deep learningcited by 3
  10. 2025Challenges in interpretability of additive modelscited by 3
  11. 2025Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practicescited by 3
  12. 2025Additive Models Explained: A Computational Complexity Approachcited by 3
18 more
  1. 2024NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shapecited by 2
  2. 2026ElliCE: Efficient and Provably Robust Algorithmic Recourse via the Rashomon Setscited by 2
  3. 2026Using predictive multiplicity to measure individual performance within the AI Actcited by 2
  4. 2024Data Science Principles for Interpretable and Explainable AIcited by 1
  5. 2025Argumentative Ensembling for Robust Recourse under Model Multiplicitycited by 1
  6. 2025Leo Breiman, the Rashomon Effect, and the Occam Dilemmacited by 1
  7. 2025Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Modelscited by 1
  8. 2026Why are there many equally good models? An Anatomy of the Rashomon Effectcited by 1
  9. 2026DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Explorationcited by 1
  10. 2026CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameterscited by 1
  11. 2024Improving Decision Sparsitycited by 0
  12. 2025VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performancecited by 0
  13. 2025From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecastingcited by 0
  14. 2025Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicitycited by 0
  15. 2025The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learningcited by 0
  16. 2026Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Seriescited by 0
  17. 2026Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugscited by 0
  18. 2026Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learningcited by 0