mlsanityData to 7 Oct 2026

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations

arXiv 2012.03058 ↗First posted 5 Dec 2020cs.AIcited by 51

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Abstract (arXiv)

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI -- which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e.g., a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 12; 2024: 6; 2025: 9; 2026: 11.

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  1. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  2. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  3. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
  4. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  5. 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Valuescited by 36
  6. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  7. 2022SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretabilitycited by 28
  8. 2022A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metricscited by 26
  9. 2022Counterfactual Plans under Distributional Ambiguitycited by 19
  10. 2021Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainabilitycited by 12
  11. 2023TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Modelscited by 8
  12. 2023Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanationscited by 8
38 more
  1. 2024Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Modelscited by 8
  2. 2025Which LIME should I trust? Concepts, Challenges, and Solutionscited by 7
  3. 2022Boundary-Aware Uncertainty for Feature Attribution Explainerscited by 6
  4. 2023Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attributioncited by 6
  5. 2024Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Surveycited by 6
  6. 2025Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimatorscited by 6
  7. 2025Explaining Large Language Models with gSMILEcited by 4
  8. 2025Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILEcited by 4
  9. 2021Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systemscited by 3
  10. 2022An Additive Instance-Wise Approach to Multi-class Model Interpretationcited by 3
  11. 2024Mapping the Mind of an Instruction-based Image Editing using SMILEcited by 3
  12. 2021A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machinescited by 2
  13. 2022Short-term Load Forecasting with Distributed Long Short-Term Memorycited by 2
  14. 2023A Comprehensive Survey on Deep Learning Techniques in Educational Data Miningcited by 2
  15. 2024Robustness of Explainable Artificial Intelligence in Industrial Process Modellingcited by 2
  16. 2024Learning Model Agnostic Explanations via Constraint Programmingcited by 2
  17. 2025Attribution Explanations for Deep Neural Networks: A Theoretical Perspectivecited by 2
  18. 2026Concerning Uncertainty -- A Systematic Survey of Uncertainty-Aware XAIcited by 2
  19. 2022Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learningcited by 1
  20. 2023Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposalcited by 1
  21. 2023CLIMAX: An exploration of Classifier-Based Contrastive Explanationscited by 1
  22. 2025VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosiscited by 1
  23. 2025Quantifying Fidelity: A Decisive Feature Approach to Comparing Synthetic and Real Imagerycited by 1
  24. 2021Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning modelscited by 0
  25. 2022Causality-Aware Local Interpretable Model-Agnostic Explanationscited by 0
  26. 2023EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanationscited by 0
  27. 2023Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizerscited by 0
  28. 2025ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settingscited by 0
  29. 2025Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AIcited by 0
  30. 2026LIME-LLM: Probing Models with Fluent Counterfactuals, Not Broken Textcited by 0
  31. 2026Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanationscited by 0
  32. 2026Enhancing LIME using Neural Decision Treescited by 0
  33. 2026Uncertainty Gating for Cost-Aware Explainable Artificial Intelligencecited by 0
  34. 2026Explanation-Guided Medical Named Entity Recognition with Stability and Boundary Awareness for Atopic Dermatitiscited by 0
  35. 2026ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AIcited by 0
  36. 2026Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matterscited by 0
  37. 2026Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendationscited by 0
  38. 2026When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanationscited by 0

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