{"total":25,"rows":[{"id":"2211.00541","title":"Logic-Based Explainability in Machine Learning","created":"2022-10-24","cited_by_count":48,"area":null},{"id":"2302.08160","title":"The Inadequacy of Shapley Values for Explainability","created":"2023-02-16","cited_by_count":31,"area":null},{"id":"2212.01051","title":"VeriX: Towards Verified Explainability of Deep Neural Networks","created":"2022-12-02","cited_by_count":30,"area":null},{"id":"2210.15675","title":"Feature Necessity & Relevancy in ML Classifier Explanations","created":"2022-10-27","cited_by_count":23,"area":null},{"id":"2406.02981","title":"Local vs. Global Interpretability: A Computational Complexity Perspective","created":"2024-06-05","cited_by_count":23,"area":null},{"id":"2212.05990","title":"On Computing Probabilistic Abductive Explanations","created":"2022-12-12","cited_by_count":18,"area":null},{"id":"2306.15272","title":"Delivering Inflated Explanations","created":"2023-06-27","cited_by_count":16,"area":null},{"id":"2506.08505","title":"Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations","created":"2025-06-10","cited_by_count":13,"area":null},{"id":"2302.02104","title":"HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline","created":"2023-02-04","cited_by_count":12,"area":null},{"id":"2306.01744","title":"Disproving XAI Myths with Formal Methods -- Initial Results","created":"2023-05-13","cited_by_count":12,"area":null},{"id":"2502.03391","title":"Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons","created":"2025-02-05","cited_by_count":12,"area":null},{"id":"2307.03380","title":"On Formal Feature Attribution and Its Approximation","created":"2023-07-07","cited_by_count":11,"area":null},{"id":"2408.03915","title":"Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation","created":"2024-08-07","cited_by_count":11,"area":null},{"id":"2304.14760","title":"A New Class of Explanations for Classifiers with Non-Binary Features","created":"2023-04-28","cited_by_count":8,"area":null},{"id":"2207.04748","title":"On Computing Relevant Features for Explaining NBCs","created":"2022-07-11","cited_by_count":5,"area":null},{"id":"2408.04903","title":"Axiomatic Characterisations of Sample-based Explainers","created":"2024-08-09","cited_by_count":3,"area":null},{"id":"2603.10661","title":"FAME: Formal Abstract Minimal Explanation for Neural Networks","created":"2026-03-11","cited_by_count":3,"area":null},{"id":"2406.11873","title":"Logic-Based Explainability: Past, Present & Future","created":"2024-06-04","cited_by_count":2,"area":null},{"id":"2407.17454","title":"Automated Explanation Selection for Scientific Discovery","created":"2024-07-24","cited_by_count":2,"area":null},{"id":"2602.18160","title":"Unifying Formal Explanations: A Complexity-Theoretic Perspective","created":"2026-02-20","cited_by_count":2,"area":null},{"id":"2303.09271","title":"Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles","created":"2023-03-16","cited_by_count":1,"area":null},{"id":"2511.03169","title":"Uncovering Bugs in Formal Explainers: A Case Study with PyXAI","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2602.17530","title":"Provably Explaining Neural Additive Models","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2306.15803","title":"On Logic-Based Explainability with Partially Specified Inputs","created":"2023-06-27","cited_by_count":0,"area":null},{"id":"2509.17774","title":"Efficient & Correct Predictive Equivalence for Decision Trees","created":"2025-09-22","cited_by_count":0,"area":null}],"next_cursor":null}