HAOTENG YIN

PhD Candidate @ Purdue

I am Haoteng Yin (殷浩腾), a PhD candidate at CS@Purdue, supervised by Prof. Pan Li. Prior to this study, I attained MPhil in Data Science at Peking University and received BEng in Computer Science from Taishan College (honors school), Shandong University.

My research interests focus on machine learning algorithms and applications in,

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Selected Publications [more]

* indicates equal contribution. 

[C1] Tianyi Zhang*, Haoteng Yin*, Rongzhe Wei, Pan Li, Anshumali Shrivastava. (2024) Learning Scalable Structural Link Representations with Bloom Signatures. In WWW'24. [Code]

[C2] Haoteng Yin, Muhan Zhang, Jianguo Wang, Pan Li. (2023) SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning. In VLDB'23. [Code]

[C3] Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, Pan Li. (2022) Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning. In VLDB'22. [Code]

[C4] Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li. (2022) Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective. In NeurIPS'22. [Code]

[C5] Haorui Wang, Haoteng Yin, Muhan Zhang, Pan Li. (2022) Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks. In ICLR'22. [Code]

[W1] Prasita Mukherjee, Haoteng Yin. (2023) OCTAL: Graph Representation Learning for LTL Model Checking. In DLG-KDD'23. Workshop. (Contributed Talk)

[W2] Haoteng Yin, Yanbang Wang, Pan Li. (2020) Revisiting graph neural networks and distance encoding from a practical view. In DLG-AAAI'21. Workshop. [Code]

[W3] Rongzhe Wei, Eleonora Kreačić, Haoyu Wang, Haoteng Yin, Eli Chien, Vamsi K Potluru, Pan Li. (2023) On the Inherent Privacy Properties of Discrete Denoising Diffusion Models. In WSDAIF-ICAIF'23. Workshop. (Oral)

[C6] Wei Xiang*, Haoteng Yin*, He Wang, Xiaogang Jin. (2024) SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents. In AAAI'24.

[C7] Bing Yu*, Haoteng Yin*,  Zhanxing Zhu. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI'18. [Dataset (PeMSD7-M/L)] [Code]

[J1] Haoteng Yin, Yang Liu. (2017). Semantic analysis of spatial temporal trajectory in LBSNs. (in Chinese) SCIENTIA SINICA Informationis, 47(8), 1051-1065. (SMP'16 Best Paper Award)

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