mlsanityData to 7 Oct 2026

Efficient Exploration of the Rashomon Set of Rule Set Models

arXiv 2406.03059 ↗First posted 5 Jun 2024cs.LGcited by 10

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Abstract (arXiv)

Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

18 of 52 references.

References · 18

  1. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  2. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  3. 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
  4. 2019Predictive Multiplicity in Classificationcited by 162
  5. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  6. 2019Optimal Sparse Decision Treescited by 104
  7. 2019On the Existence of Simpler Machine Learning Modelscited by 87
  8. 2022Exploring the Whole Rashomon Set of Sparse Decision Treescited by 80
  9. 2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
  10. 2017Learning Certifiably Optimal Rule Lists for Categorical Datacited by 50
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  12. 2015Learning Optimized Or's of And'scited by 13
6 more
  1. 2020Diverse Rule Setscited by 12
  2. 2022Computing the Collection of Good Models for Rule Listscited by 12
  3. 2013A Scalable and Nearly Uniform Generator of SAT Witnessescited by 10
  4. 2014Balancing Scalability and Uniformity in SAT Witness Generatorcited by 5
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  6. 2018Constrained Counting and Sampling: Bridging the Gap between Theory and Practicecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 2.

10 papers.

  1. 2025Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Timecited by 13
  2. 2024Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspectivecited by 7
  3. 2025SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumerationcited by 5
  4. 2025Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterizationcited by 4
  5. 2025Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithmscited by 1
  6. 2026CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameterscited by 1
  7. 2025MOSS: Multi-Objective Optimization for Stable Rule Setscited by 0
  8. 2025VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performancecited by 0
  9. 2025Efficient & Correct Predictive Equivalence for Decision Treescited by 0
  10. 2026Falling Trees: A Model Class for Interpretable Risk Prioritizationcited by 0