Fangxin Wang

12 papers A* 2B 1Journal 8Unranked 1
YearRankTypeTitle / Venue / Authors
2025 A* conf
BANGS: Game-theoretic Node Selection for Graph Self-Training.
ICLR
Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu
2025 J jnl
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization.
CoRR
Langzhou He, Junyou Zhu, Fangxin Wang, Junhua Liu, Haoyan Xu, Yue Zhao, Philip S. Yu, Qitian Wu
2025 B conf
Confidence-Aware Fine-Tuning of Sequential Recommendation Systems Via Conformal Prediction.
IEEE Big Data
Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Philip S. Yu
2025 J jnl
Topology-Aware Conformal Prediction for Stream Networks.
CoRR
Jifan Zhang, Fangxin Wang, Philip S. Yu, Kaize Ding, Shixiang Zhu
2024 J jnl
BANGS: Game-Theoretic Node Selection for Graph Self-Training.
CoRR
Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu
2024 J jnl
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction.
CoRR
Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Kay Liu, Philip S. Yu
2024 — conf
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models.
LoG
Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu
2024 J jnl
Uncertainty in Graph Neural Networks: A Survey.
CoRR
Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu
2024 J jnl
Uncertainty in Graph Neural Networks: A Survey.
Trans. Mach. Learn. Res.
Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu
2023 J jnl
Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models.
CoRR
Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu
2023 A* conf
Equal Opportunity of Coverage in Fair Regression.
NeurIPS
Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu
2023 J jnl
Equal Opportunity of Coverage in Fair Regression.
CoRR
Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu