Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. - Bing Yu; Haoteng Yin; Zhanxing Zhu (CoRR, 2017)

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. - Bing Yu; Haoteng Yin; Zhanxing Zhu (IJCAI, 2018)

ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling. - Bing Yu; Haoteng Yin; Zhanxing Zhu (CoRR, 2019)

Revisit graph neural networks and distance encoding in a practical view. - Haoteng Yin; Yanbang Wang; Pan Li 005 (CoRR, 2020)

Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks. - Haorui Wang; Haoteng Yin; Muhan Zhang; Pan Li 005 (ICLR, 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)

OCTAL: Graph Representation Learning for LTL Model Checking. - Prasita Mukherjee; Haoteng Yin; Susheel Suresh; Tiark Rompf (CoRR, 2022)

Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning. - Haoteng Yin; Muhan Zhang; Yanbang Wang; Jianguo Wang 001; Pan Li 005 (Proc. VLDB Endow., 2022)

OCTAL: Graph Representation Learning for LTL Model Checking. - Prasita Mukherjee; Haoteng Yin (CoRR, 2023)

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)

SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning. - Haoteng Yin; Muhan Zhang; Jianguo Wang 001; Pan Li 005 (Proc. VLDB Endow., 2023)

SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents. - Wei Xiang; Haoteng Yin; He Wang 002; Xiaogang Jin 001 (AAAI, 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)

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

How to Talk to Language Models: Serialization Strategies for Structured Entity Matching. - Haoteng Yin; Jinha Kim; Prashant Mathur; Krishanu Sarker; Vidit Bansal (NAACL (Findings), 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)