Explainable Machine Learning with Prior Knowledge: An Overview. - Katharina Beckh; Sebastian Müller; Matthias Jakobs; Vanessa Toborek; Hanxiao Tan; Raphael Fischer 001; Pascal Welke; Sebastian Houben; Laura von Rüden (CoRR, 2021)

Surrogate Model-Based Explainability Methods for Point Cloud NNs. - Hanxiao Tan; Helena Kotthaus (WACV, 2022)

Maximum Entropy Baseline for Integrated Gradients. - Hanxiao Tan (IJCNN, 2023)

The Generalizability of Explanations. - Hanxiao Tan (IJCNN, 2023)

Harnessing Prior Knowledge for Explainable Machine Learning: An Overview. - Katharina Beckh; Sebastian Müller; Matthias Jakobs; Vanessa Toborek; Hanxiao Tan; Raphael Fischer 001; Pascal Welke; Sebastian Houben; Laura von Rüden (SaTML, 2023)

Visualizing Global Explanations of Point Cloud DNNs. - Hanxiao Tan (WACV, 2023)

Explainability-Aware One Point Attack for Point Cloud Neural Networks. - Hanxiao Tan; Helena Kotthaus (WACV, 2023)

Evaluating Explanation Robustness to Model Pruning. - Hanxiao Tan (IJCNN, 2024)

DAM: Diffusion Activation Maximization for 3D Global Explanations. - Hanxiao Tan (CoRR, 2024)

Flow AM: Generating Point Cloud Global Explanations by Latent Alignment. - Hanxiao Tan (CoRR, 2024)

Evaluating Sensitivity Consistency of Explanations. - Hanxiao Tan (WACV, 2025)