6 papers · 1 filter
GNN Explanations that do not Explain and How to find Them
Steve Azzolin, Stefano Teso, Bruno Lepri +2
Explanations provided by Self-explainable Graph Neural Networks (SE-GNNs) are fundamental for understanding the model's inner workings and for identifying potential misuse of sensi…
Is BatchEnsemble a Single Model? On Calibration and Diversity of Efficient Ensembles
Anton Zamyatin, Patrick Indri, Sagar Malhotra +1
In resource-constrained and low-latency settings, uncertainty estimates must be efficiently obtained. Deep Ensembles provide robust epistemic uncertainty (EU) but require training…
Probably Approximately Global Robustness Certification
Peter Blohm, Patrick Indri, Thomas Gärtner +1
We propose and investigate probabilistic guarantees for the adversarial robustness of classification algorithms. While traditional formal verification approaches for robustness are…
Prime Implicant Explanations for Reaction Feasibility Prediction
Klaus Weinbauer, Tieu-Long Phan, Peter F. Stadler +2
Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often…
Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective
Steve Azzolin, Sagar Malhotra, Andrea Passerini +1
Self-Explainable Graph Neural Networks (SE-GNNs) are popular explainable-by-design GNNs, but their explanations' properties and limitations are not well understood. Our first contr…
Logical Distillation of Graph Neural Networks
Alexander Pluska, Pascal Welke, Thomas Gärtner +1
We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections…