{"total":19,"rows":[{"id":"2410.02331","title":"Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks","created":"2024-10-03","cited_by_count":18,"area":null},{"id":"2302.03225","title":"Efficient XAI Techniques: A Taxonomic Survey","created":"2023-02-07","cited_by_count":14,"area":null},{"id":"2304.12943","title":"Generating robust counterfactual explanations","created":"2023-04-24","cited_by_count":13,"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":"2410.17781","title":"Evaluating Explanations Through LLMs: Beyond Traditional User Studies","created":"2024-10-23","cited_by_count":8,"area":null},{"id":"2312.11747","title":"Robust Stochastic Graph Generator for Counterfactual Explanations","created":"2023-12-18","cited_by_count":7,"area":null},{"id":"2402.01408","title":"Counterfactual Concept Bottleneck Models","created":"2024-02-02","cited_by_count":7,"area":null},{"id":"2308.08945","title":"Interpretable Graph Neural Networks for Tabular Data","created":"2023-08-17","cited_by_count":5,"area":null},{"id":"2409.12952","title":"The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations","created":"2024-09-19","cited_by_count":5,"area":null},{"id":"2405.15632","title":"Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning","created":"2024-05-24","cited_by_count":4,"area":null},{"id":"2505.04775","title":"Prediction via Shapley Value Regression","created":"2025-05-07","cited_by_count":3,"area":null},{"id":"2309.04284","title":"Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers","created":"2023-09-08","cited_by_count":2,"area":null},{"id":"2409.12883","title":"Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition","created":"2024-09-19","cited_by_count":1,"area":null},{"id":"2504.20667","title":"Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2507.17161","title":"Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection","created":"2025-07-23","cited_by_count":1,"area":null},{"id":"2405.20685","title":"Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2508.10455","title":"RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2508.14482","title":"On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselines","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2607.01306","title":"PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations","created":"2026-07-01","cited_by_count":0,"area":null}],"next_cursor":null}