Equal Opportunity of Coverage in Fair Regression. - Fangxin Wang 003; Lu Cheng 001; Ruocheng Guo; Kay Liu; Philip S. Yu (NeurIPS, 2023)

Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models. - Kay Liu; Hengrui Zhang; Ziqing Hu; Fangxin Wang 003; Philip S. Yu (CoRR, 2023)

Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models. - Kay Liu; Hengrui Zhang; Ziqing Hu; Fangxin Wang 003; Philip S. Yu (LoG, 2024)

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction. - Chen Wang 018; Fangxin Wang 003; Ruocheng Guo; Yueqing Liang; Kay Liu; Philip S. Yu (CoRR, 2024)

Uncertainty in Graph Neural Networks: A Survey. - Fangxin Wang 003; Yuqing Liu 003; Kay Liu; Yibo Wang 001; Sourav Medya; Philip S. Yu (Trans. Mach. Learn. Res., 2024)

BANGS: Game-theoretic Node Selection for Graph Self-Training. - Fangxin Wang 003; Kay Liu; Sourav Medya; Philip S. Yu (ICLR, 2025)

Topology-Aware Conformal Prediction for Stream Networks. - Jifan Zhang; Fangxin Wang 003; Philip S. Yu; Kaize Ding; Shixiang Zhu (CoRR, 2025)