| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |
|---|---|---|---|---|---|---|
| 3 | Vilone G., Longo L. | Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees | eXplainable Artificial Intelligence, The World Conference (xAI-2025) | 2025 |   
@InProceedings{ViloneLongo2025, author="Vilone, Giulia and Longo, Luca", editor="Guidotti, Riccardo and Schmid, Ute and Longo, Luca", title="Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees", booktitle="Explainable Artificial Intelligence", year="2026", publisher="Springer Nature Switzerland", address="Cham", pages="89--112", isbn="978-3-032-08333-3" } [Close]
| Logical Analysis • Graph Theory • Graph Theory in Probability • Machine Learning • Reasoning • Symbolic AI • Explainable AI • Surrogate models • Computational Argumentation • Rule-based systems • Decision-trees • Dense Neural Networks • Deep learning |
| 2 | Raufi B., Finnegan C., Longo L. | A Comparative Analysis of SHAP, LIME, ANCHORS, and DICE for Interpreting a Dense Neural Network in Credit Card Fraud Detection | eXplainable Artificial Intelligence, The World Conference (xAI-2024) | 2024 | 
@InProceedings{10.1007/978-3-031-63803-9_20, author="Raufi, Bujar and Finnegan, Ciaran and Longo, Luca", editor="Longo, Luca and Lapuschkin, Sebastian and Seifert, Christin", title="A Comparative Analysis of SHAP, LIME, ANCHORS, and DICE for Interpreting a Dense Neural Network in Credit Card Fraud Detection", booktitle="Explainable Artificial Intelligence", year="2024", publisher="Springer Nature Switzerland", address="Cham", pages="365--383", isbn="978-3-031-63803-9" } [Close]
| Explainable Artificial Intelligence •
Credit Card Fraud Detection •
Interpretability •
methods comparison •
SHapley Additive exPlanations •
Local Interpretable •
Model-agnostic Explanation •
ANCHORS •
Diverse Counterfactual Explanations |
| 1 | Chikkankod A.V., Longo L. | On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps | Machine Learning and Knowledge Extraction | 2022 |
@Article{VinayakLongo2022, AUTHOR = {Chikkankod, Arjun Vinayak and Longo, Luca}, TITLE = {On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps}, JOURNAL = {Machine Learning and Knowledge Extraction}, VOLUME = {4}, YEAR = {2022}, NUMBER = {4}, PAGES = {1042--1064}, URL = {https://www.mdpi.com/2504-4990/4/4/53}, ISSN = {2504-4990},, DOI = {10.3390/make4040053} } [Close]
| electroencephalography • latent space analysis • sliding windowing • convolutional autoencoders • automatic feature extraction • dense neural network |
| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |