Yuxuan Liang

Selected publications [See more]

* denotes corresponding author and + indicates equal contribution.

1. ICLR’23

   [New] Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective

   Kun Wang, Yuxuan Liang*, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, and Yang Wang*

   In The International Conference on Learning Representations, 2023

   Bib

   @inproceedings{wang2023searching,
     title = {Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective},
     author = {Wang, Kun and Liang*, Yuxuan and Wang, Pengkun and Wang, Xu and Gu, Pengfei and Fang, Junfeng and Wang*, Yang},
     booktitle = {The International Conference on Learning Representations},
     year = {2023}
   }

2. AAAI’23

   [New] AirFormer: Predicting Nationwide Air Quality in China with Transformers

   Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann

   In Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

   Bib PDF Code

   @inproceedings{liang2023airformer,
     title = {AirFormer: Predicting Nationwide Air Quality in China with Transformers},
     author = {Liang, Yuxuan and Xia, Yutong and Ke, Songyu and Wang, Yiwei and Wen, Qingsong and Zhang, Junbo and Zheng, Yu and Zimmermann, Roger},
     booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence},
     year = {2023}
   }

3. SIGSPATIAL’22

   When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?

   Xu Liu+, Yuxuan Liang+, Chao Huang, Yu Zheng, Bryan Hooi, and Roger Zimmermann

   In Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

   Bib PDF Code

   @inproceedings{liu2022contrastive,
     title = {When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?},
     author = {Liu+, Xu and Liang+, Yuxuan and Huang, Chao and Zheng, Yu and Hooi, Bryan and Zimmermann, Roger},
     booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
     pages = {1--12},
     year = {2022}
   }

4. WWW’21

   Fine-grained Urban Flow Prediction

   Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David Rosenblum, and Roger Zimmermann

   In Proceedings of the Web Conference, 2021

   Bib PDF Cited by 22

   @inproceedings{liang2021fine,
     title = {Fine-grained Urban Flow Prediction},
     author = {Liang, Yuxuan and Ouyang, Kun and Sun, Junkai and Wang, Yiwei and Zhang, Junbo and Zheng, Yu and Rosenblum, David and Zimmermann, Roger},
     booktitle = {Proceedings of the Web Conference},
     pages = {1833--1845},
     year = {2021}
   }

5. KDD’19

   Urbanfm: Inferring Fine-Grained Urban Flows

   Yuxuan Liang+, Kun Ouyang+, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, and Yu Zheng

   In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019

   Bib PDF Code Cited by 80

   @inproceedings{liang2019urbanfm,
     title = {Urbanfm: Inferring Fine-Grained Urban Flows},
     author = {Liang+, Yuxuan and Ouyang+, Kun and Jing, Lin and Ruan, Sijie and Liu, Ye and Zhang, Junbo and Rosenblum, David S and Zheng, Yu},
     booktitle = {Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
     pages = {3132--3142},
     year = {2019}
   }

6. IJCAI’18

   GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.

   Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng

   In International Joint Conference on Artificial Intelligence, 2018

   Bib PDF Code Cited by 357

   @inproceedings{liang2018geoman,
     title = {GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.},
     author = {Liang, Yuxuan and Ke, Songyu and Zhang, Junbo and Yi, Xiuwen and Zheng, Yu},
     booktitle = {International Joint Conference on Artificial Intelligence},
     volume = {2018},
     pages = {3428--3434},
     year = {2018}
   }

7. KDD’19

   Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

   Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang

   In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019

   Bib PDF Code Cited by 268

   @inproceedings{pan2019urban,
     title = {Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning},
     author = {Pan, Zheyi and Liang, Yuxuan and Wang, Weifeng and Yu, Yong and Zheng, Yu and Zhang, Junbo},
     booktitle = {Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
     pages = {1720--1730},
     year = {2019}
   }

8. IJCAI’16

   Urban Water Quality Prediction based on Multi-Task Multi-View Learning

   Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, and David S Rosenblum

   In Proceedings of the 25th international joint conference on artificial intelligence, 2016

   Bib PDF Code Cited by 208

   @inproceedings{liu2016urban,
     title = {Urban Water Quality Prediction based on Multi-Task Multi-View Learning},
     author = {Liu, Ye and Zheng, Yu and Liang, Yuxuan and Liu, Shuming and Rosenblum, David S},
     booktitle = {Proceedings of the 25th international joint conference on artificial intelligence},
     year = {2016}
   }

9. NeurIPS’20

   Digraph Inception Convolutional Networks

   Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim

   Advances in neural information processing systems 2020

   Bib PDF Cited by 43

   @article{tong2020digraph,
     title = {Digraph Inception Convolutional Networks},
     author = {Tong, Zekun and Liang, Yuxuan and Sun, Changsheng and Li, Xinke and Rosenblum, David and Lim, Andrew},
     journal = {Advances in neural information processing systems},
     volume = {33},
     pages = {17907--17918},
     year = {2020}
   }