3 citations · 4 across the 11 of their papers we have counts for
8 papers · 1 filter
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
Dong Li, Zhengzhang Chen, Xujiang Zhao +5
Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise…
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation
Tao Yin, Chen Zhao, Xiaoyan Liu +1
Graph neural networks (GNNs) are proven effective in extracting complex node and structural information from graph data. While current GNNs perform well in node classification task…
MLDGG: Meta-Learning for Domain Generalization on Graphs
Qin Tian, Chen Zhao, Minglai Shao +3
Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testin…
GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models
Zhixia He, Chen Zhao, Minglai Shao +3
Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most exis…
Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
Dong Li, Chen Zhao, Minglai Shao +1
Achieving the generalization of an invariant classifier from training domains to shifted test domains while simultaneously considering model fairness is a substantial and complex c…
FADE: Towards Fairness-aware Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models
Yujie Lin, Dong Li, Minglai Shao +2
Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Tra…