Unsupervised Conditional Adversarial Networks for Tax Evasion Detection. - Rongzhe Wei; Bo Dong 001; Qinghua Zheng; Xulyu Zhu; Jianfei Ruan; Huan He (IEEE BigData, 2019)

ABR-HIC: Attention Based Bidirectional RNN for Hierarchical Industry Classification. - Rongzhe Wei; Qinghua Zheng; Bo Dong 001; Kuanzheng Yang; Huan He; Jianfei Ruan (IEEE BigData, 2019)

TEDM-PU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning. - Yingchao Wu; Qinghua Zheng; Yuda Gao; Bo Dong 001; Rongzhe Wei; Fa Zhang 002; Huan He (IEEE BigData, 2019)

Dual Adversarial Networks for Land-Cover Classification. - Jingyi An; Rongzhe Wei; Zhichao Zha; Bo Dong (COMPSAC, 2020)

NEUD-TRI: Network Embedding Based on Upstream and Downstream for Transaction Risk Identification. - Jingyi An; Qinghua Zheng; Rongzhe Wei; Bo Dong 001; Xuanya Li (COMPSAC, 2020)

A Novel Tax Evasion Detection Framework via Fused Transaction Network Representation. - Yingchao Wu; Bo Dong 001; Qinghua Zheng; Rongzhe Wei; Zhiwen Wang; Xuanya Li (COMPSAC, 2020)

DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching. - Rongzhe Wei; Fa Zhang 002; Bo Dong 001; Qinghua Zheng (CoRR, 2020)

SLA2P: Self-supervised Anomaly Detection with Adversarial Perturbation. - Yizhou Wang 006; Can Qin; Rongzhe Wei; Yi Xu 005; Yue Bai; Yun Fu 001 (CoRR, 2021)

Self-supervision Meets Adversarial Perturbation: A Novel Framework for Anomaly Detection. - Yizhou Wang 006; Can Qin; Rongzhe Wei; Yi Xu 005; Yue Bai; Yun Fu 001 (CIKM, 2022)

Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. - Rongzhe Wei; Haoteng Yin; Junteng Jia; Austin R. Benson; Pan Li 005 (NeurIPS, 2022)

On the Inherent Privacy Properties of Discrete Denoising Diffusion Models. - Rongzhe Wei; Eleonora Kreacic; Haoyu Wang 004; Haoteng Yin; Eli Chien; Vamsi K. Potluru; Pan Li 005 (CoRR, 2023)

Differentially Private Graph Diffusion with Applications in Personalized PageRanks. - Rongzhe Wei; Eli Chien; Pan Li 005 (NeurIPS, 2024)

Learning Scalable Structural Representations for Link Prediction with Bloom Signatures. - Tianyi Zhang 011; Haoteng Yin; Rongzhe Wei; Pan Li 005; Anshumali Shrivastava (WWW, 2024)

Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing. - Shuaiqi Wang; Rongzhe Wei; Mohsen Ghassemi; Eleonora Kreacic; Vamsi K. Potluru (CoRR, 2024)

Privately Learning from Graphs with Applications in Fine-tuning Large Language Models. - Haoteng Yin; Rongzhe Wei; Eli Chien; Pan Li 005 (CoRR, 2024)

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning. - Rongzhe Wei; Mufei Li; Mohsen Ghassemi; Eleonora Kreacic; Yifan Li; Xiang Yue; Bo Li; Vamsi K. Potluru; Pan Li 005; Eli Chien (CoRR, 2024)

On the Inherent Privacy Properties of Discrete Denoising Diffusion Models. - Rongzhe Wei; Eleonora Kreacic; Haoyu Peter Wang; Haoteng Yin; Eli Chien; Vamsi K. Potluru; Pan Li 005 (Trans. Mach. Learn. Res., 2024)

SLA$^{{ text{2}}}$2P: Self-Supervised Anomaly Detection With Adversarial Perturbation. - Yizhou Wang 006; Can Qin; Rongzhe Wei; Yi Xu 005; Yue Bai; Yun Fu 001 (IEEE Trans. Knowl. Data Eng., 2024)

Model Generalization on Text Attribute Graphs: Principles with Large Language Models. - Haoyu Wang 004; Shikun Liu; Rongzhe Wei; Pan Li 005 (CoRR, 2025)

Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness. - Rongzhe Wei; Peizhi Niu; Hans Hao-Hsun Hsu; Ruihan Wu; Haoteng Yin; Mohsen Ghassemi; Yifan Li; Vamsi K. Potluru; Eli Chien; Kamalika Chaudhuri; Olgica Milenkovic; Pan Li 005 (CoRR, 2025)

Differentially Private Relational Learning with Entity-level Privacy Guarantees. - Yinan Huang; Haoteng Yin; Eli Chien; Rongzhe Wei; Pan Li 005 (CoRR, 2025)