3 citations · 8 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Graphs Generalization under Distribution Shifts
Qin Tian, Wenjun Wang, Chen Zhao +3
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the…
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao +3
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced…