Papers
Timur Sattarov, Dayananda Herurkar and Jörn Hees. Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
Junqi Jiang, Francesco Leofante, Antonio Rago and Francesca Toni. Formalising the Robustness of Counterfactual Explanations for Neural Networks
Mattia Jacopo Villani, Joshua Lockhart and Daniele Magazzeni. Feature Importance for Time Series Data: Improving KernelSHAP
Yulin Liu and Luyao Zhang. Cryptocurrency Valuation: An Explainable AI Approach
Natraj Raman, Daniele Magazzeni and Sameena Shah. Bayesian Hierarchical Models for Counterfactual Estimation
Hamed Ayoobi, Nico Potyca and Francesca Toni. Structurally Faithful Argumentative Explanations for Neural Networks
Federico Sabbatini and Roberta Calegari. Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper