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cs.LG2026
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
Tiejin Chen, Wenwang Huang, Linsey Pang +2
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classif…
cs.LG2025
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo +1
Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models…
cs.LG2024
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation
Jiaxing Zhang, Jiayi Liu, Dongsheng Luo +2
Recent studies seek to provide Graph Neural Network (GNN) interpretability via multiple unsupervised learning models. Due to the scarcity of datasets, current methods easily suffer…