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ORCIDxAI 2024: Valletta, Malta
- Luca Longo
, Sebastian Lapuschkin, Christin Seifert:
Explainable Artificial Intelligence - Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II. Communications in Computer and Information Science 2154, Springer 2024, ISBN 978-3-031-63796-4
XAI for Graphs and Computer Vision
- André Levi Zanon, Leonardo Chaves Dutra da Rocha, Marcelo Garcia Manzato:
Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems. 3-27 - Marta Caro-Martínez, Jose L. Jorro-Aragoneses, Belén Díaz-Agudo, Juan A. Recio-García:
Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study. 28-41 - Jonas Amling, Stephan Scheele, Emanuel Slany, Moritz Lang, Ute Schmid:
Explainable AI for Mixed Data Clustering. 42-62 - Michele Fontanesi, Alessio Micheli, Marco Podda:
Explaining Graph Classifiers by Unsupervised Node Relevance Attribution. 63-74 - Mandani Ntekouli, Gerasimos Spanakis, Lourens J. Waldorp, Anne Roefs:
Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention. 75-99 - Angeliki Dimitriou, Nikolaos Chaidos, Maria Lymperaiou, Giorgos Stamou:
Graph Edits for Counterfactual Explanations: A Comparative Study. 100-112 - Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller:
Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. 113-129 - Sara Pohland, Claire J. Tomlin:
Understanding the Dependence of Perception Model Competency on Regions in an Image. 130-154 - Syed Nouman Hasany, Fabrice Mériaudeau, Caroline Petitjean:
A Guided Tour of Post-hoc XAI Techniques in Image Segmentation. 155-177 - Alessio Borriero, Martina Milazzo, Matteo Diano, Davide Orsenigo, Maria-Chiara Villa, Chiara Di Fazio, Marco Tamietto, Alan Perotti:
Explainable Emotion Decoding for Human and Computer Vision. 178-201 - Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer:
Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification. 202-216
Logic, Reasoning, and Rule-Based Explainable AI
- Clemens Dubslaff, Verena Klös, Juliane Päßler:
Template Decision Diagrams for Meta Control and Explainability. 219-242 - Stipe Pandzic, Joris Graff:
A Logic of Weighted Reasons for Explainable Inference in AI. 243-267 - George Theodorou, Sophia Karagiorgou, Annamaria Fulignoli, Roberto Magri:
On Explaining and Reasoning About Optical Fiber Link Problems. 268-289 - Björn-Hergen Laabs, Lea L. Kronziel, Inke R. König, Silke Szymczak:
Construction of Artificial Most Representative Trees by Minimizing Tree-Based Distance Measures. 290-310 - Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares, Sylvio Barbon Junior:
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles. 311-332
Model-Agnostic and Statistical Methods for eXplainable AI
- Valentina Ghidini, Michael D. Multerer, Jacopo Quizi, Rohan Sen:
Observation-Specific Explanations Through Scattered Data Approximation. 335-345 - Weronika Hryniewska-Guzik, Luca Longo, Przemyslaw Biecek:
CNN-Based Explanation Ensembling for Dataset, Representation and Explanations Evaluation. 346-368 - Amir Hossein Akhavan Rahnama, Judith Bütepage, Henrik Boström:
Local List-Wise Explanations of LambdaMART. 369-392 - Isel Grau, Gonzalo Nápoles:
Sparseness-Optimized Feature Importance. 393-415 - Jeremy Goldwasser, Giles Hooker:
Stabilizing Estimates of Shapley Values with Control Variates. 416-439 - Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König:
A Guide to Feature Importance Methods for Scientific Inference. 440-464 - David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer:
Interpretable Machine Learning for TabPFN. 465-476 - Valentina Ghidini:
Statistics and Explainability: A Fruitful Alliance. 477-488 - Patrick Kolpaczki, Georg Haselbeck, Eyke Hüllermeier:
How Much Can Stratification Improve the Approximation of Shapley Values? 489-512
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