Ping Xiong
Ping Xiong
Doctoral Researcher
M.Sc. Industrial Engineering, TU Berlin
B.Sc. Industrial Engineering, TU Berlin
Member of BIFOLD Graduate School
- Explainable AI
- Graph Neural Network
- Bayesian Learning
- Applications
Haci Ismail Aslan, Philipp Wiesner, Ping Xiong, Odej Kao
β-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation
Haci Ismail Aslan, Philipp Wiesner, Ping Xiong, Odej Kao
-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation
Thomas Schnake, Farnoush Rezaei Jafaria, Jonas Lederer, Ping Xiong, Shinichi Nakajima, Stefan Gugler, Grégoire Montavon, Klaus-Robert Müller
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus Robert Müller, Shinichi Nakajima
Relevant Walk Search for Explaining Graph Neural Networks
Ping Xiong, Thomas Schnake, Gregoire Montavon, Klaus-Robert Muller, Shinichi Nakajima
Efficient Higher-Order Subgraph Attribution via Message Passing
Symbolic XAI
Researchers at BIFOLD have been exploring how to make AI explain itself in the same way, people explain themselves. The team’s work focuses on making AI predictions as clear and intuitive as a human explanation.