Explainable Artificial Intelligence

.- XAI for graphs and Computer vision.
.- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.
.- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.
.- Explainable AI for Mixed Data Clustering.
.- Explaining graph classifiers by unsupervised node relevance attribution.
.- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.
.- Graph Edits for Counterfactual Explanations: A comparative study.
.- Model guidance via explanations turns image classifiers into segmentation models.
.- Understanding the Dependence of Perception Model Competency on Regions in an Image.
.- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.
.- Explainable Emotion Decoding for Human and Computer Vision.
.- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
.- Logic, reasoning, and rule-based explainable AI.
.- Template Decision Diagrams for Meta Control and Explainability.
.- A Logic of Weighted Reasons for Explainable Inference in AI.
.- On Explaining and Reasoning about Fiber Optical Link Problems.
.- Construction of artificial most representative trees by minimizing tree-based distance measures.
.- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.
.- Model-agnostic and statistical methods for eXplainable AI.
.- Observation-specific explanations through scattered data approximation.
.- CNN-based explanation ensembling for dataset, representation and explanations evaluation.
.- Local List-wise Explanations of LambdaMART.
.- Sparseness-Optimized Feature Importance.
.- Stabilizing Estimates of Shapley Values with Control Variates.
.- A Guide to Feature Importance Methods for Scientific Inference.
.- Interpretable Machine Learning for TabPFN.
.- Statistics and explainability: a fruitful alliance.
.- How Much Can Stratification Improve the Approximation of Shapley Values?.