mlsanityData to 14 Oct 2026

VeriX: Towards Verified Explainability of Deep Neural Networks

arXiv 2212.01051 ↗First posted 2 Dec 2022cs.LGcited by 30

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Abstract (arXiv)

We present VeriX (Verified eXplainability), a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models. We build such explanations and counterfactuals iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking. We evaluate our method on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 39 of its 74 references that are in the map. First posted on arXiv 2 Dec 2022.

Builds on

39 of 74 references.

References · 39

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  5. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  6. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 445
  7. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
  8. 2016Safety Verification of Deep Neural Networkscited by 263
  9. 2017Formal Verification of Piece-Wise Linear Feed-Forward Neural Networkscited by 242
  10. 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 228
  11. 2019Algorithms for Verifying Deep Neural Networkscited by 221
  12. 2018Formal Security Analysis of Neural Networks using Symbolic Intervalscited by 185
27 more
  1. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 185
  2. 2020Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Reviewcited by 174
  3. 2018Efficient Formal Safety Analysis of Neural Networkscited by 165
  4. 2018Abduction-Based Explanations for Machine Learning Modelscited by 160
  5. 2019Branch and Bound for Piecewise Linear Neural Network Verificationcited by 138
  6. 2018A Symbolic Approach to Explaining Bayesian Network Classifierscited by 131
  7. 2020On The Reasons Behind Decisionscited by 100
  8. 2022Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Boundcited by 92
  9. 2021PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximationscited by 77
  10. 2020Verification of Deep Convolutional Neural Networks Using ImageStarscited by 75
  11. 2020Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delaycited by 57
  12. 2021On Explaining Random Forests with SATcited by 57
  13. 2021On Guaranteed Optimal Robust Explanations for NLP Modelscited by 54
  14. 2022On Tackling Explanation Redundancy in Decision Treescited by 44
  15. 2019Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustnesscited by 40
  16. 2020Parallelization Techniques for Verifying Neural Networkscited by 36
  17. 2018A Game-Based Approximate Verification of Deep Neural Networks with Provable Guaranteescited by 33
  18. 2022ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanationscited by 26
  19. 2021Probabilistic Sufficient Explanationscited by 24
  20. 2022Efficient Neural Network Analysis with Sum-of-Infeasibilitiescited by 24
  21. 2018Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Correctionscited by 19
  22. 2020Validation of Image-Based Neural Network Controllers through Adaptive Stress Testingcited by 19
  23. 2022Scalable Verification of GNN-based Job Schedulerscited by 19
  24. 2022Toward Certified Robustness Against Real-World Distribution Shiftscited by 16
  25. 2019Robustness Guarantees for Deep Neural Networks on Videoscited by 14
  26. 2020Provably Robust Adversarial Examplescited by 8
  27. 2022On Optimizing Back-Substitution Methods for Neural Network Verificationcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 12; 2026: 11.

30 papers.

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  1. 2024Marabou 2.0: A Versatile Formal Analyzer of Neural Networkscited by 81
  2. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
  3. 2023From Robustness to Explainability and Back Againcited by 14
  4. 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasonscited by 12
  5. 2024Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretationcited by 11
  6. 2026Formal Mechanistic Interpretability: Automated Circuit Discovery with Provable Guaranteescited by 11
  7. 2024Formal Explanations for Neuro-Symbolic AIcited by 6
  8. 2025Self-Explaining Neural Networks for Business Process Monitoringcited by 5
  9. 2025Space Explanations of Neural Network Classificationcited by 5
  10. 2025Faster Verified Explanations for Neural Networkscited by 4
  11. 2025Additive Models Explained: A Computational Complexity Approachcited by 3
  12. 2026FAME: Formal Abstract Minimal Explanation for Neural Networkscited by 3
18 more
  1. 2026Unifying Formal Explanations: A Complexity-Theoretic Perspectivecited by 2
  2. 2026Incremental Neural Network Verification via Learned Conflictscited by 2
  3. 2024Efficient Contrastive Explanations on Demandcited by 1
  4. 2025Uncovering Bugs in Formal Explainers: A Case Study with PyXAIcited by 1
  5. 2025Neurosymbolic Inference On Foundation Models For Remote Sensing Text-to-image Retrieval With Complex Queriescited by 1
  6. 2026Provably Explaining Neural Additive Modelscited by 1
  7. 2024Unveiling Transformer Perception by Exploring Input Manifoldscited by 0
  8. 2025Fixed Point Explainabilitycited by 0
  9. 2025Comparing Neural Network Encodings for Logic-based Explainabilitycited by 0
  10. 2025The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verificationcited by 0
  11. 2025Rigorous Feature Importance Scores based on Shapley Value and Banzhaf Indexcited by 0
  12. 2025Efficient & Correct Predictive Equivalence for Decision Treescited by 0
  13. 2026Towards Rigorous Explainability by Feature Attributioncited by 0
  14. 2026Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certificationscited by 0
  15. 2026Mining Verdict Boundaries for Neural Network Verificationcited by 0
  16. 2026Beyond L2: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architecturescited by 0
  17. 2026Neuron Activation-based Computation of Logical Explanations for Deep Neural Networkscited by 0
  18. 2026The Misery of Mechanistic Interpretability: A Formal Perspectivecited by 0