Publications

Listed by categories in reversed chronological order, where + indicates equal contribution and * denotes corresponding author.

Dr. Yuxuan Liang has published some papers in refereed journals and conferences, including DM venues (e.g., KDD*16, WWW*7, ICDE*3, TKDE*7, TMC*1), AI venues (e.g., TPAMI*2, AI Journal*2, NeurIPS*10, ICML*3, IJCAI*7, AAAI*11, ICLR*4), and CV venues (e.g., ECCV*1, MM*4). Here are some representative papers:

Spatio-Temporal (ST) Data Mining:
- LLM/FMs for Time Series and ST Data: UniTraj [arXiv], Time-FFM [NeurIPS’24], FM4TS Survey [KDD’24], Position Paper [ICML’24a], LLM4TS Survey [Survey’24], UrbanCLIP [WWW’24a], UniTime [WWW’24b], Time-LLM [ICLR’24]
- ST Expert Models: PatchSTG [KDD’25], Attraos [NeurIPS’24], ST Causal Learning [NeurIPS’23 & KDD’23], DiffSTG [SIGSPATIAL’23], STGNP [KDD’23], MixRNN [TKDE’22], STGCL [SIGSPATIAL’22], ST-MetaNet [TKDE’20, KDD’19], STRN [WWW’21],
  UrbanFM [KDD’19, TKDE’20], GeoMAN [IJCAI’18],
- Multimodal/Foundation Models for ST Data: UrbanVLP [AAAI’25], GeoHG [arXiv], UrbanCross [MM’24], UrbanCLIP [WWW’24] DualFormer [ECCV’22], [MM’22], [MM’21]
- Trajectory Data Mining: Diffusion Model [KDD’24], Causal Learning [IJCAI’24], COLA [WWW’24], TrajCL [ICDE’23], TrajFormer [CIKM’22], TrajODE [IJCAI’21]
- AutoML for ST data: AutoSTG+ [AI’23], AutoSTG [WWW’21], AutoST [KDD’21]
- Applications: Nationwide Air Quality Inference [AAAI’25], Nationwide Air Quality Forecasting [AAAI’23], Resource Allocation [UBICOMP’21], ST Visualization [TVCG’21], Map Recovery [AAAI’20], Traffic Congestion [SIGSPATIAL’17],
Graph Mining:
- Graph learning: [AAAI’25], [NeurIPS’24], [TKDE’24], [ICML’24b], [ICML’24c], [TPAMI’24], [ICLR’24], [ICLR’23], [WWW’21], [NeurIPS’21], [NeurIPS’20]
- Graph augmentation: [WWW’21], [NeurIPS’21], [KDD’20]
- Applications: [KDD’22]
Survey Paper:
- LLMs/FMs + ST data:
  - [KDD’24] Foundation Models -> Time Series Analysis
  - [arXiv] Large Models -> Time Series and Spatio-Temporal Data
- ST Data Mining & Urban Computing:
  - [TKDE’23] Spatio-Temporal Graph Neural Networks -> Urban Computing
  - [arXiv] Deep Learning -> Trajectory Data Management and Mining
  - [arXiv] Deep Learning -> Cross-Domain Data Fusion in Urban Computing
  - [arXiv] Diffusion Models -> Time Series and Spatio-Temporal Data
  - [arXiv] Service Route and Time Prediction in Instant Delivery
- Time Series Analysis:
  - [TPAMI’24] Self-Supervised Learning -> Time Series Analysis
  - [arXiv] Deep Learning -> Multivariate Time Series Imputation

2025

AAAI’25

[New] AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

Qiongyan Wang, Yutong Xia, Siru Zhong, Weichuang Li, Yuankai Wu, Shifen Cheng, Junbo Zhang, Yu Zheng, and Yuxuan Liang*

In Thirty-Nineth AAAI Conference on Artificial Intelligence, 2025

@inproceedings{wang2025inferring,
  title = {AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks},
  author = {Wang, Qiongyan and Xia, Yutong and Zhong, Siru and Li, Weichuang and Wu, Yuankai and Cheng, Shifen and Zhang, Junbo and Zheng, Yu and Liang*, Yuxuan},
  booktitle = {Thirty-Nineth AAAI Conference on Artificial Intelligence},
  year = {2025}
}

AAAI’25

[New] UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction

Xixuan Hao, Wei Chen, Yibo Yan, Siru Zhong, Kun Wang, Qingsong Wen, and Yuxuan Liang*

In Thirty-Nineth AAAI Conference on Artificial Intelligence, 2025

@inproceedings{hao2025urbanvlp,
  title = {UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction},
  author = {Hao, Xixuan and Chen, Wei and Yan, Yibo and Zhong, Siru and Wang, Kun and Wen, Qingsong and Liang*, Yuxuan},
  booktitle = {Thirty-Nineth AAAI Conference on Artificial Intelligence},
  year = {2025}
}

AAAI’25

[New] Unlocking the Power of LSTM for Long Term Time Series Forecasting

Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren, Yuxuan Liang*, Peng Sun*, and Qingsong Wen

In Thirty-Nineth AAAI Conference on Artificial Intelligence, 2025

@inproceedings{kong2025unlocking,
  title = {Unlocking the Power of LSTM for Long Term Time Series Forecasting},
  author = {Kong, Yaxuan and Wang, Zepu and Nie, Yuqi and Zhou, Tian and Zohren, Stefan and Liang*, Yuxuan and Sun*, Peng and Wen, Qingsong},
  booktitle = {Thirty-Nineth AAAI Conference on Artificial Intelligence},
  year = {2025}
}

AAAI’25

[New] Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification

Yutong Xia, Runpeng Yu, Yuxuan Liang*, Xavier Bresson, Xinchao Wang*, and Roger Zimmermann

In Thirty-Nineth AAAI Conference on Artificial Intelligence, 2025

@inproceedings{xia2025through,
  title = {Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification},
  author = {Xia, Yutong and Yu, Runpeng and Liang*, Yuxuan and Bresson, Xavier and Wang*, Xinchao and Zimmermann, Roger},
  booktitle = {Thirty-Nineth AAAI Conference on Artificial Intelligence},
  year = {2025}
}

AAAI’25

[New] UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks

Jie Zhao, Chao Chen, Yuanshao Zhu, Mingyu Deng, and Yuxuan Liang

In Thirty-Nineth AAAI Conference on Artificial Intelligence, 2025

@inproceedings{zhao2025unified,
  title = {UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks},
  author = {Zhao, Jie and Chen, Chao and Zhu, Yuanshao and Deng, Mingyu and Liang, Yuxuan},
  booktitle = {Thirty-Nineth AAAI Conference on Artificial Intelligence},
  year = {2025}
}

AAAI’25

[New] Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

Qingxiang Liu, Sheng Sun, Yuxuan Liang, Jingjing Xue, and Min Liu

In Thirty-Nineth AAAI Conference on Artificial Intelligence, 2025

@inproceedings{liu2025personalized,
  title = {Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach},
  author = {Liu, Qingxiang and Sun, Sheng and Liang, Yuxuan and Xue, Jingjing and Liu, Min},
  booktitle = {Thirty-Nineth AAAI Conference on Artificial Intelligence},
  year = {2025}
}

KDD’25

[New] Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective

Yuchen Fang, Yuxuan Liang, Bo Hui, Zezhi Shao, Liwei Deng, Xu Liu, Xinke Jiang, and Kai Zheng

In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

@inproceedings{fang2025efficient,
  title = {Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective},
  author = {Fang, Yuchen and Liang, Yuxuan and Hui, Bo and Shao, Zezhi and Deng, Liwei and Liu, Xu and Jiang, Xinke and Zheng, Kai},
  booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2025}
}

KDD’25

[New] DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting

Hao Wu, Haomin Wen, Guibin Zhang, Yutong Xia, Yuxuan Liang, Yu Zheng, Qingsong Wen, and Kun Wang

In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

@inproceedings{wu2025dynamic,
  title = {DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting},
  author = {Wu, Hao and Wen, Haomin and Zhang, Guibin and Xia, Yutong and Liang, Yuxuan and Zheng, Yu and Wen, Qingsong and Wang, Kun},
  booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2025}
}

2024

KDD’24

Foundation models for time series analysis: A tutorial and survey

Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, and Qingsong Wen*

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Tutorial Track Paper), 2024

Bib PDF Cited by 73

@inproceedings{liang2024foundation,
  title = {Foundation models for time series analysis: A tutorial and survey},
  author = {Liang, Yuxuan and Wen, Haomin and Nie, Yuqi and Jiang, Yushan and Jin, Ming and Song, Dongjin and Pan, Shirui and Wen*, Qingsong},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Tutorial Track Paper)},
  year = {2024}
}

[New] A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation

Xiaoyue Luo, Shifen Cheng, Lizeng Wang, Yuxuan Liang, and Feng Lu

International Journal of Geographical Information Science 2024

@article{luo2024tensor,
  title = {A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation},
  author = {Luo, Xiaoyue and Cheng, Shifen and Wang, Lizeng and Liang, Yuxuan and Lu, Feng},
  journal = {International Journal of Geographical Information Science},
  pages = {1--29},
  year = {2024},
  publisher = {Taylor \& Francis}
}

[New] Rethinking Urban Mobility Prediction: A Multivariate Time Series Forecasting Approach

Jinguo Cheng, Ke Li, Yuxuan Liang, Lijun Sun, Junchi Yan, and Yuankai Wu

IEEE Transactions on Intelligent Transportation Systems 2024

@article{cheng2024rethinking,
  title = {Rethinking Urban Mobility Prediction: A Multivariate Time Series Forecasting Approach},
  author = {Cheng, Jinguo and Li, Ke and Liang, Yuxuan and Sun, Lijun and Yan, Junchi and Wu, Yuankai},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  year = {2024},
  publisher = {IEEE}
}

[New] REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting

Qingxiang Liu, Sheng Sun, Yuxuan Liang, Xiaolong Xu, Min Liu, Muhammad Bilal, Yuwei Wang, Xujing Li, and Yu Zheng

IEEE Transactions on Intelligent Transportation Systems 2024

@article{liu2024refol,
  title = {REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting},
  author = {Liu, Qingxiang and Sun, Sheng and Liang, Yuxuan and Xu, Xiaolong and Liu, Min and Bilal, Muhammad and Wang, Yuwei and Li, Xujing and Zheng, Yu},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  year = {2024},
  publisher = {IEEE}
}

NeurIPS’24

Attractor memory for long-term time series forecasting: A chaos perspective

Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, and Yuxuan Liang*

In Advances in neural information processing systems, 2024

@inproceedings{hu2024attractor,
  title = {Attractor memory for long-term time series forecasting: A chaos perspective},
  author = {Hu, Jiaxi and Hu, Yuehong and Chen, Wei and Jin, Ming and Pan, Shirui and Wen, Qingsong and Liang*, Yuxuan},
  booktitle = {Advances in neural information processing systems},
  year = {2024},
}

NeurIPS’24

Terra: A Multimodal Spatio-Temporal Dataset Spanning the Earth (Datasets and Benchmarks Track Paper)

Wei Chen, Xixuan Hao, Yuankai Wu, and Yuxuan Liang*

In Advances in neural information processing systems, 2024

@inproceedings{chen2024terra,
  title = {Terra: A Multimodal Spatio-Temporal Dataset Spanning the Earth (Datasets and Benchmarks Track Paper)},
  author = {Chen, Wei and Hao, Xixuan and Wu, Yuankai and Liang*, Yuxuan},
  booktitle = {Advances in neural information processing systems},
  year = {2024}
}

NeurIPS’24

Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting

Qingxiang Liu, Xu Liu, Chenghao Liu, Qingsong Wen, and Yuxuan Liang*

In Advances in neural information processing systems, 2024

@inproceedings{liu2024time,
  title = {Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting},
  author = {Liu, Qingxiang and Liu, Xu and Liu, Chenghao and Wen, Qingsong and Liang*, Yuxuan},
  booktitle = {Advances in neural information processing systems},
  year = {2024},
}

NeurIPS’24

GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning

Guibin Zhang, Haonan Dong, Yuchen Zhang, Zhixun Li, Dingshuo Chen, Kai Wang, Tianlong Chen, Yuxuan Liang, Dawei Cheng, and Kun Wang

In Advances in neural information processing systems, 2024

@inproceedings{zhang2024gder,
  title = {GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning},
  author = {Zhang, Guibin and Dong, Haonan and Zhang, Yuchen and Li, Zhixun and Chen, Dingshuo and Wang, Kai and Chen, Tianlong and Liang, Yuxuan and Cheng, Dawei and Wang, Kun},
  booktitle = {Advances in neural information processing systems},
  year = {2024}
}

NeurIPS’24

Improving Generalization of Dynamic Graph Learning via Environment Prompt

Kuo Yang, Zhengyang Zhou, Qihe Huang, Limin Li, Yuxuan Liang, and Yang Wang

In Advances in neural information processing systems, 2024

@inproceedings{yang2024improving,
  title = {Improving Generalization of Dynamic Graph Learning via Environment Prompt},
  author = {Yang, Kuo and Zhou, Zhengyang and Huang, Qihe and Li, Limin and Liang, Yuxuan and Wang, Yang},
  booktitle = {Advances in neural information processing systems},
  year = {2024}
}

SIGSPATIAL

Towards unifying diffusion models for probabilistic spatio-temporal graph learning

Junfeng Hu, Xu Liu, Zhencheng Fan, Yuxuan Liang*, and Roger Zimmermann

In Proceedings of the 32nd International Conference on Advances in Geographic Information Systems, 2024

@inproceedings{hu2024towards,
  title = {Towards unifying diffusion models for probabilistic spatio-temporal graph learning},
  author = {Hu, Junfeng and Liu, Xu and Fan, Zhencheng and Liang*, Yuxuan and Zimmermann, Roger},
  booktitle = {Proceedings of the 32nd International Conference on Advances in Geographic Information Systems},
  year = {2024},
}

Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook

Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu, Haomin Wen, Erdong Liu, Junbo Zhang, Yong Li, Tianrui Li, Yu Zheng, and Yuxuan Liang*

Information Fusion 2024

@article{zou2024deep,
  title = {Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook},
  author = {Zou, Xingchen and Yan, Yibo and Hao, Xixuan and Hu, Yuehong and Wen, Haomin and Liu, Erdong and Zhang, Junbo and Li, Yong and Li, Tianrui and Zheng, Yu and Liang*, Yuxuan},
  journal = {Information Fusion},
  year = {2024},
}

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

Haomin Wen, Youfang Lin, Lixia Wu, Xiaowei Mao, Tianyue Cai, Yunfeng Hou, Shengnan Guo, Yuxuan Liang, Guangyin Jin, Yiji Zhao, and others

IEEE Transactions on Knowledge and Data Engineering 2024

@article{wen2024survey,
  title = {A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects},
  author = {Wen, Haomin and Lin, Youfang and Wu, Lixia and Mao, Xiaowei and Cai, Tianyue and Hou, Yunfeng and Guo, Shengnan and Liang, Yuxuan and Jin, Guangyin and Zhao, Yiji and others},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2024},
  publisher = {IEEE},
}

MM’24

UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation

Siru Zhong, Xixuan Hao, Yibo Yan, Ying Zhang, Yangqiu Song, and Yuxuan Liang*

In ACM Multimedia, 2024

@inproceedings{zhong2024urbancross,
  title = {UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation},
  author = {Zhong, Siru and Hao, Xixuan and Yan, Yibo and Zhang, Ying and Song, Yangqiu and Liang*, Yuxuan},
  booktitle = {ACM Multimedia},
  year = {2024}
}

On regularization for explaining graph neural networks: An information theory perspective

Junfeng Fang, Guibin Zhang, Kun Wang*, Wenjie Du, Yifan Duan, Yuankai Wu, Roger Zimmermann, Xiaowen Chu, and Yuxuan Liang*

IEEE Transactions on Knowledge and Data Engineering 2024

@article{fang2024regularization,
  title = {On regularization for explaining graph neural networks: An information theory perspective},
  author = {Fang, Junfeng and Zhang, Guibin and Wang*, Kun and Du, Wenjie and Duan, Yifan and Wu, Yuankai and Zimmermann, Roger and Chu, Xiaowen and Liang*, Yuxuan},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2024},
  publisher = {IEEE}
}

ECML-PKDD’24

Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency

Xu Liu, Yuxuan Liang*, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, and Roger Zimmermann

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2024

@inproceedings{liu2024reinventing,
  title = {Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency},
  author = {Liu, Xu and Liang*, Yuxuan and Huang, Chao and Hu, Hengchang and Cao, Yushi and Hooi, Bryan and Zimmermann, Roger},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  year = {2024}
}

KDD’24

Controltraj: Controllable trajectory generation with topology-constrained diffusion model

Yuanshao Zhu, James Jianqiao Yu*, Xiangyu Zhao*, Qidong Liu, Yongchao Ye, Wei Chen, Zijian Zhang, Xuetao Wei, and Yuxuan Liang*

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

@inproceedings{zhu2024controltraj,
  title = {Controltraj: Controllable trajectory generation with topology-constrained diffusion model},
  author = {Zhu, Yuanshao and Yu*, James Jianqiao and Zhao*, Xiangyu and Liu, Qidong and Ye, Yongchao and Chen, Wei and Zhang, Zijian and Wei, Xuetao and Liang*, Yuxuan},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2024}
}

KDD’24

The Heterophily Snowflake Hypothesis: Training and Empowering GNN for Heterophilic Graphs

Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Xiaojiang Peng, Yuxuan Liang*, and Yang Wang*

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

@inproceedings{wang2024heterophily,
  title = {The Heterophily Snowflake Hypothesis: Training and Empowering GNN for Heterophilic Graphs},
  author = {Wang, Kun and Li, Guohao and Wang, Shilong and Zhang, Guibin and Wang, Kai and You, Yang and Peng, Xiaojiang and Liang*, Yuxuan and Wang*, Yang},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2024}
}

KDD’24

The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive field

Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Xiaojiang Peng, Yuxuan Liang*, and Yang Wang*

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

@inproceedings{wang2024snowflake,
  title = {The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive field},
  author = {Wang, Kun and Li, Guohao and Wang, Shilong and Zhang, Guibin and Wang, Kai and You, Yang and Peng, Xiaojiang and Liang*, Yuxuan and Wang*, Yang},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2024}
}

KDD’24

Cluster-Wide Task Slowdown Detection

Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, and Shuiguang Deng

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

@inproceedings{chen2024cluster,
  title = {Cluster-Wide Task Slowdown Detection},
  author = {Chen, Feiyi and Zhang, Yingying and Fan, Lunting and Liang, Yuxuan and Pang, Guansong and Wen, Qingsong and Deng, Shuiguang},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2024}
}

KDD’24

LaDe: The first comprehensive last-mile delivery dataset from industry

Lixia Wu, Haomin Wen, Haoyuan Hu, Xiaowei Mao, Yutong Xia, Ergang Shan, Jianbin Zhen, Junhong Lou, Yuxuan Liang*, Liuqing Yang, and others

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

@inproceedings{wu2024lade,
  title = {LaDe: The first comprehensive last-mile delivery dataset from industry},
  author = {Wu, Lixia and Wen, Haomin and Hu, Haoyuan and Mao, Xiaowei and Xia, Yutong and Shan, Ergang and Zhen, Jianbin and Lou, Junhong and Liang*, Yuxuan and Yang, Liuqing and others},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2024}
}

ICML’24

Position Paper: What Can Large Language Models Tell Us about Time Series Analysis

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang*, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen*

In International Conference on Machine Learning, 2024

@inproceedings{jin2024position,
  title = {Position Paper: What Can Large Language Models Tell Us about Time Series Analysis},
  author = {Jin, Ming and Zhang, Yifan and Chen, Wei and Zhang, Kexin and Liang*, Yuxuan and Yang, Bin and Wang, Jindong and Pan, Shirui and Wen*, Qingsong},
  booktitle = {International Conference on Machine Learning},
  year = {2024}
}

ICML’24

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, and Yang You

In International Conference on Machine Learning, 2024

@inproceedings{zhang2024navigating,
  title = {Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching},
  author = {Zhang, Yuchen and Zhang, Tianle and Wang, Kai and Guo, Ziyao and Liang, Yuxuan and Bresson, Xavier and Jin, Wei and You, Yang},
  booktitle = {International Conference on Machine Learning},
  year = {2024}
}

ICML’24

Two heads are better than one: Boosting graph sparse training via semantic and topological awareness

Guibin Zhang, Yanwei Yue, Kun Wang, Junfeng Fang, Yongduo Sui, Kai Wang, Yuxuan Liang, Dawei Cheng, Shirui Pan, and Tianlong Chen

In International Conference on Machine Learning, 2024

@inproceedings{zhang2024two,
  title = {Two heads are better than one: Boosting graph sparse training via semantic and topological awareness},
  author = {Zhang, Guibin and Yue, Yanwei and Wang, Kun and Fang, Junfeng and Sui, Yongduo and Wang, Kai and Liang, Yuxuan and Cheng, Dawei and Pan, Shirui and Chen, Tianlong},
  booktitle = {International Conference on Machine Learning},
  year = {2024}
}

IJCAI’24

Spatio-Temporal Field Neural Networks for Air Quality Inference

Yutong Feng, Qiongyan Wang, Yutong Xia, Junlin Huang, Siru Zhong, and Yuxuan Liang*

In International Joint Conference on Artificial Intelligence, 2024

@inproceedings{feng2024spatio,
  title = {Spatio-Temporal Field Neural Networks for Air Quality Inference},
  author = {Feng, Yutong and Wang, Qiongyan and Xia, Yutong and Huang, Junlin and Zhong, Siru and Liang*, Yuxuan},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year = {2024}
}

IJCAI’24

Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach

Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang, Zekun Tong, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang*

In International Joint Conference on Artificial Intelligence, 2024

@inproceedings{zhang2024predicting,
  title = {Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach},
  author = {Zhang, Huaiwu and Xia, Yutong and Zhong, Siru and Wang, Kun and Tong, Zekun and Wen, Qingsong and Zimmermann, Roger and Liang*, Yuxuan},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year = {2024}
}

IJCAI’24

Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning.

Kang Luo, Yuanshao Zhu, Wei Chen, Kun Wang, Zhengyang Zhou, Sijie Ruan, and Yuxuan Liang*

In International Joint Conference on Artificial Intelligence, 2024

@inproceedings{luo2024towards,
  title = {Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning.},
  author = {Luo, Kang and Zhu, Yuanshao and Chen, Wei and Wang, Kun and Zhou, Zhengyang and Ruan, Sijie and Liang*, Yuxuan},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year = {2024}
}

Self-supervised learning for time series analysis: Taxonomy, progress, and prospects

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, and Shirui Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence 2024

@article{zhang2024self,
  title = {Self-supervised learning for time series analysis: Taxonomy, progress, and prospects},
  author = {Zhang, Kexin and Wen, Qingsong and Zhang, Chaoli and Cai, Rongyao and Jin, Ming and Liu, Yong and Zhang, James and Liang, Yuxuan and Pang, Guansong and Song, Dongjin and Pan, Shirui},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2024},
  publisher = {IEEE}
}

Semantic-fused multi-granularity cross-city traffic prediction

Kehua Chen, Yuxuan Liang, Jindong Han, Siyuan Feng, Meixin Zhu, and Hai Yang

Transportation Research Part C: Emerging Technologies 2024

@article{chen2024semantic,
  title = {Semantic-fused multi-granularity cross-city traffic prediction},
  author = {Chen, Kehua and Liang, Yuxuan and Han, Jindong and Feng, Siyuan and Zhu, Meixin and Yang, Hai},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume = {162},
  pages = {104604},
  year = {2024},
  publisher = {Elsevier}
}

Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts

Kun Wang, Hao Wu, Guibin Zhang, Junfeng Fang, Yuxuan Liang*, Yuankai Wu, Roger Zimmermann, and Yang Wang*

IEEE Transactions on Knowledge and Data Engineering 2024

@article{wang2024modeling,
  title = {Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts},
  author = {Wang, Kun and Wu, Hao and Zhang, Guibin and Fang, Junfeng and Liang*, Yuxuan and Wu, Yuankai and Zimmermann, Roger and Wang*, Yang},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2024},
  publisher = {IEEE}
}

WWW’24

UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web

Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang*

In The Web Conference, 2024

Bib PDF Cited by 44

@inproceedings{yan2024urbanclip,
  title = {UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web},
  author = {Yan, Yibo and Wen, Haomin and Zhong, Siru and Chen, Wei and Chen, Haodong and Wen, Qingsong and Zimmermann, Roger and Liang*, Yuxuan},
  booktitle = {The Web Conference},
  year = {2024}
}

WWW’24

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang*, Bryan Hooi, and Roger Zimmermann

In The Web Conference, 2024

Bib PDF Cited by 57

@inproceedings{liu2014unitime,
  title = {UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting},
  author = {Liu, Xu and Hu, Junfeng and Li, Yuan and Diao, Shizhe and Liang*, Yuxuan and Hooi, Bryan and Zimmermann, Roger},
  booktitle = {The Web Conference},
  year = {2024}
}

WWW’24

COLA: Cross-city Mobility Transformer for Human Trajectory Simulation

Yu Wang, Tongya Zheng, Yuxuan Liang, Shunyu Liu, and Mingli Song

In The Web Conference, 2024

@inproceedings{wang2024cola,
  title = {COLA: Cross-city Mobility Transformer for Human Trajectory Simulation},
  author = {Wang, Yu and Zheng, Tongya and Liang, Yuxuan and Liu, Shunyu and Song, Mingli},
  booktitle = {The Web Conference},
  year = {2024}
}

ICLR’24

NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling

Kun Wang, Hao Wu, Yifan Duan, Guibin Zhang, Kai Wang, Xiaojiang Peng, Yu Zheng, Yuxuan Liang*, and Yang Wang*

In The International Conference on Learning Representations, 2024

@inproceedings{wang2024nuwa,
  title = {NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling},
  author = {Wang, Kun and Wu, Hao and Duan, Yifan and Zhang, Guibin and Wang, Kai and Peng, Xiaojiang and Zheng, Yu and Liang*, Yuxuan and Wang*, Yang},
  booktitle = {The International Conference on Learning Representations},
  year = {2024}
}

ICLR’24

Graph Lottery Ticket Automated

Guibin Zhang, Kun Wang, Wei Huang, Yanwei Yue, Yang Wang, Roger Zimmermann, Aojun Zhou, Dawei Cheng, Jin Zeng*, and Yuxuan Liang*

In The International Conference on Learning Representations, 2024

@inproceedings{zhang2024graph,
  title = {Graph Lottery Ticket Automated},
  author = {Zhang, Guibin and Wang, Kun and Huang, Wei and Yue, Yanwei and Wang, Yang and Zimmermann, Roger and Zhou, Aojun and Cheng, Dawei and Zeng*, Jin and Liang*, Yuxuan},
  booktitle = {The International Conference on Learning Representations},
  year = {2024}
}

ICLR’24

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen

In The International Conference on Learning Representations, 2024

@inproceedings{jin2024time,
  title = {Time-LLM: Time Series Forecasting by Reprogramming Large Language Models},
  author = {Jin, Ming and Wang, Shiyu and Ma, Lintao and Chu, Zhixuan and Zhang, James Y. and Shi, Xiaoming and Chen, Pin-Yu and Liang, Yuxuan and Li, Yuan-Fang and Pan, Shirui and Wen, Qingsong},
  booktitle = {The International Conference on Learning Representations},
  year = {2024}
}

AAAI’24

Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model

Hao Wu, Shilong Wang, Yuxuan Liang, Zhengyang Zhou, Wei Huang, Wei Xiong, and Kun Wang

In Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

@inproceedings{wu2024earthfarseer,
  title = {Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model},
  author = {Wu, Hao and Wang, Shilong and Liang, Yuxuan and Zhou, Zhengyang and Huang, Wei and Xiong, Wei and Wang, Kun},
  booktitle = {Thirty-Eighth AAAI Conference on Artificial Intelligence},
  year = {2024}
}

AAAI’24

MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting

Wanlin Cai, Yuxuan Liang, Xianggen Liu, Jianshuai Feng, and Yuankai Wu*

In Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

@inproceedings{cai2024msgnet,
  title = {MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting},
  author = {Cai, Wanlin and Liang, Yuxuan and Liu, Xianggen and Feng, Jianshuai and Wu*, Yuankai},
  booktitle = {Thirty-Eighth AAAI Conference on Artificial Intelligence},
  year = {2024}
}

AAAI’24

SENCR: A Span Enhanced Two-stage Network with Counterfactual Rethinking for Chinese NER

Hang Zheng, Qingsong Li, Shen Chen, Yuxuan Liang, and Li Liu*

In Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

@inproceedings{zheng2024sencr,
  title = {SENCR: A Span Enhanced Two-stage Network with Counterfactual Rethinking for Chinese NER},
  author = {Zheng, Hang and Li, Qingsong and Chen, Shen and Liang, Yuxuan and Liu*, Li},
  booktitle = {Thirty-Eighth AAAI Conference on Artificial Intelligence},
  year = {2024}
}

ICASSP’24

Fall Prediction by a Spatio-Temporal Multi-Channel Causal Model from Wearable Sensors Data

Guorui Liao, Jiawei Liu, Yuxuan Liang, Shu Wang, and Li Liu*

In IEEE ICASSP, 2024

@inproceedings{liao2024fall,
  title = {Fall Prediction by a Spatio-Temporal Multi-Channel Causal Model from Wearable Sensors Data},
  author = {Liao, Guorui and Liu, Jiawei and Liang, Yuxuan and Wang, Shu and Liu*, Li},
  booktitle = {IEEE ICASSP},
  year = {2024}
}

ICDE’24

Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Anomaly Detection

Feiyi Chen, Yingying Zhang, Zhen Qin, Lunting Fan, Renhe Jiang, Yuxuan Liang, Qingsong Wen, and Shuiguang Deng

In The 40th IEEE International Conference on Data Engineering, 2024

@inproceedings{chen2024learning,
  title = {Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Anomaly Detection},
  author = {Chen, Feiyi and Zhang, Yingying and Qin, Zhen and Fan, Lunting and Jiang, Renhe and Liang, Yuxuan and Wen, Qingsong and Deng, Shuiguang},
  booktitle = {The 40th IEEE International Conference on Data Engineering},
  year = {2024}
}

ICDE’24

Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning

Shuliang Wang, Song Tang, Sijie Ruan*, Cheng Long, Yuxuan Liang, Qi Li, Ziqiang Yuan, Jie Bao, and Yu Zheng

In The 40th IEEE International Conference on Data Engineering, 2024

@inproceedings{wang2024urban,
  title = {Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning},
  author = {Wang, Shuliang and Tang, Song and Ruan*, Sijie and Long, Cheng and Liang, Yuxuan and Li, Qi and Yuan, Ziqiang and Bao, Jie and Zheng, Yu},
  booktitle = {The 40th IEEE International Conference on Data Engineering},
  year = {2024}
}

WSDM’24

CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting

Chengxin Wang, Yuxuan Liang, and Gary Tan

In The 17th ACM International Conference on Web Search and Data Mining, 2024

2023

AAAI’23

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 Cited by 100

@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}
}

End-to-end Delay Modeling via Leveraging Competitive Interaction among Network Flows

Weiping Zheng, Minli Hong, Ruihao Ye, Xiaomao Fan, Yuxuan Liang, Gansen Zhao, and Roger Zimmermann

IEEE Transactions on Network and Service Management 2023

@article{zheng2023end,
  title = {End-to-end Delay Modeling via Leveraging Competitive Interaction among Network Flows},
  author = {Zheng, Weiping and Hong, Minli and Ye, Ruihao and Fan, Xiaomao and Liang, Yuxuan and Zhao, Gansen and Zimmermann, Roger},
  journal = {IEEE Transactions on Network and Service Management},
  year = {2023},
  publisher = {IEEE}
}

Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets

Kun Wang, Yuxuan Liang*, Xinglin Li, Guohao Li, Bernard Ghanem, Roger Zimmermann, Zhengyang Zhou, huahui Yi, Yudong Zhang, and Yang Wang*

IEEE Transactions on Pattern Analysis and Machine Intelligence 2023

@article{wang2023brave,
  title = {Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets},
  author = {Wang, Kun and Liang*, Yuxuan and Li, Xinglin and Li, Guohao and Ghanem, Bernard and Zimmermann, Roger and Zhou, Zhengyang and Yi, huahui and Zhang, Yudong and Wang*, Yang},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2023},
  publisher = {IEEE}
}

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

Guangyin Jin, Yuxuan Liang*, Yuchen Fang, Jincai Huang, Junbo Zhang, and Yu Zheng

IEEE Transactions on Knowledge and Data Engineering 2023

@article{jin2023spatio,
  title = {Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey},
  author = {Jin, Guangyin and Liang*, Yuxuan and Fang, Yuchen and Huang, Jincai and Zhang, Junbo and Zheng, Yu},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2023},
  publisher = {IEEE}
}

NeurIPS’23

Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

Yutong Xia, Yuxuan Liang*, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, and Roger Zimmermann

In Advances in neural information processing systems, 2023

PDF

EMNLP’23

Primacy Effect of ChatGPT

Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang, and Bryan Hooi

In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

@inproceedings{wang2023primacy,
  title = {Primacy Effect of ChatGPT},
  author = {Wang, Yiwei and Cai, Yujun and Chen, Muhao and Liang, Yuxuan and Hooi, Bryan},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  year = {2023}
}

NeurIPS’23

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting (Datasets and Benchmarks Track Paper)

Xu Liu, Yutong Xia, Yuxuan Liang*, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann

In Advances in neural information processing systems, 2023

PDF Code

SIGSPATIAL’23

DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models

Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang*

In Proceedings of the 31st International Conference on Advances in Geographic Information Systems, 2023

@inproceedings{wen2023diffstg,
  title = {DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models},
  author = {Wen, Haomin and Lin, Youfang and Xia, Yutong and Wan, Huaiyu and Wen, Qingsong and Zimmermann, Roger and Liang*, Yuxuan},
  booktitle = {Proceedings of the 31st International Conference on Advances in Geographic Information Systems},
  year = {2023}
}

MM’23

PetalView: Fine-grained Location and Orientation Extraction of Street-view Images via Cross-view Local Search

Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Xianjing Han, Hannes Kruppa, See-Kiong Ng, and Roger Zimmermann

In ACM Multimedia, 2023

@inproceedings{hu2023petalview,
  title = {PetalView: Fine-grained Location and Orientation Extraction of Street-view Images via Cross-view Local Search},
  author = {Hu, Wenmiao and Zhang, Yichen and Liang, Yuxuan and Yin, Yifang and Han, Xianjing and Kruppa, Hannes and Ng, See-Kiong and Zimmermann, Roger},
  booktitle = {ACM Multimedia},
  year = {2023}
}

Predicting collective human mobility via countering spatiotemporal heterogeneity

Zhengyang Zhou, Kuo Yang, Yuxuan Liang, Binwu Wang, Hongyang Chen, and Yang Wang

IEEE Transactions on Mobile Computing 2023

@article{zhou2023predicting,
  title = {Predicting collective human mobility via countering spatiotemporal heterogeneity},
  author = {Zhou, Zhengyang and Yang, Kuo and Liang, Yuxuan and Wang, Binwu and Chen, Hongyang and Wang, Yang},
  journal = {IEEE Transactions on Mobile Computing},
  year = {2023}
}

Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference

Junfeng Hu, Yuxuan Liang*, Zhencheng Fan, Yifang Yin, Ying Zhang, and Roger Zimmermann

IEEE Transactions on Neural Networks and Learning Systems 2023

@article{hu2023decoupling,
  title = {Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference},
  author = {Hu, Junfeng and Liang*, Yuxuan and Fan, Zhencheng and Yin, Yifang and Zhang, Ying and Zimmermann, Roger},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  year = {2023}
}

KDD’23

Graph Neural Processes for Spatio-Temporal Extrapolation

Junfeng Hu, Yuxuan Liang*, Zhencheng Fan, Hongyang Chen, Yu Zheng, and Roger Zimmermann

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

@inproceedings{hu2023graph,
  title = {Graph Neural Processes for Spatio-Temporal Extrapolation},
  author = {Hu, Junfeng and Liang*, Yuxuan and Fan, Zhencheng and Chen, Hongyang and Zheng, Yu and Zimmermann, Roger},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2023}
}

KDD’23

Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning

Zhengyang Zhou, Qihe Huang, Kuo Yang, Kun Wang, Xu Wang, Yudong Zhang, Yuxuan Liang, and Yang Wang

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

@inproceedings{zhou2023maintaining,
  title = {Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning},
  author = {Zhou, Zhengyang and Huang, Qihe and Yang, Kuo and Wang, Kun and Wang, Xu and Zhang, Yudong and Liang, Yuxuan and Wang, Yang},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year = {2023}
}

AI

AutoSTG+: An Automatic Framework to Discover The Optimal Network for Spatio-temporal Graph Prediction

Songyu Ke, Zheyi Pan, Tianfu He, Yuxuan Liang, Junbo Zhang, and Yu Zheng

Artificial Intelligence 2023

ICDE’23

Contrastive Trajectory Similarity Learning with Dual-Feature Attention

Yanchuan Chang, Jianzhong Qi, Yuxuan Liang, and Egemen Tanin

In The 39th IEEE International Conference on Data Engineering, 2023

@inproceedings{chang2023traj,
  title = {Contrastive Trajectory Similarity Learning with Dual-Feature Attention},
  author = {Chang, Yanchuan and Qi, Jianzhong and Liang, Yuxuan and Tanin, Egemen},
  booktitle = {The 39th IEEE International Conference on Data Engineering},
  year = {2023}
}

ICLR’23

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

@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}
}

2022

Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Zheyi Pan, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S Rosenblum, and Roger Zimmermann

IEEE Transactions on Knowledge and Data Engineering 2022

@article{liang2022mixed,
  title = {Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting},
  author = {Liang, Yuxuan and Ouyang, Kun and Wang, Yiwei and Pan, Zheyi and Yin, Yifang and Chen, Hongyang and Zhang, Junbo and Zheng, Yu and Rosenblum, David S and Zimmermann, Roger},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2022},
  publisher = {IEEE}
}

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

@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}
}

ECCV’22

Dualformer: Local-global stratified transformer for efficient video recognition

Yuxuan Liang, Pan Zhou, Roger Zimmermann, and Shuicheng Yan

In European Conference on Computer Vision, 2022

@inproceedings{liang2022dualformer,
  title = {Dualformer: Local-global stratified transformer for efficient video recognition},
  author = {Liang, Yuxuan and Zhou, Pan and Zimmermann, Roger and Yan, Shuicheng},
  booktitle = {European Conference on Computer Vision},
  pages = {577--595},
  year = {2022},
  organization = {Springer}
}

CIKM’22

TrajFormer: Efficient Trajectory Classification with Transformers

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Xu Liu, Hongyang Chen, Junbo Zhang, Yu Zheng, and Roger Zimmermann

In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

@inproceedings{liang2022trajformer,
  title = {TrajFormer: Efficient Trajectory Classification with Transformers},
  author = {Liang, Yuxuan and Ouyang, Kun and Wang, Yiwei and Liu, Xu and Chen, Hongyang and Zhang, Junbo and Zheng, Yu and Zimmermann, Roger},
  booktitle = {Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages = {1229--1237},
  year = {2022}
}

SIGSPATIAL’22

Periodic Residual Learning for Crowd Flow Forecasting

Chengxin Wang, Yuxuan Liang, and Gary Tan

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

@inproceedings{wang2022periodic,
  title = {Periodic Residual Learning for Crowd Flow Forecasting},
  author = {Wang, Chengxin and Liang, Yuxuan and Tan, Gary},
  booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
  pages = {1--10},
  year = {2022}
}

KDD’22

Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li

In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022

@inproceedings{yang2022multi,
  title = {Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation},
  author = {Yang, Yuhao and Huang, Chao and Xia, Lianghao and Liang, Yuxuan and Yu, Yanwei and Li, Chenliang},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {2263--2274},
  year = {2022}
}

MM’22

Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery

Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Andrei Georgescu, An Tran, Hannes Kruppa, See-Kiong Ng, and Roger Zimmermann

In Proceedings of the 30th ACM International Conference on Multimedia, 2022

@inproceedings{hu2022beyond,
  title = {Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery},
  author = {Hu, Wenmiao and Zhang, Yichen and Liang, Yuxuan and Yin, Yifang and Georgescu, Andrei and Tran, An and Kruppa, Hannes and Ng, See-Kiong and Zimmermann, Roger},
  booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
  pages = {6155--6164},
  year = {2022}
}

Access

Content-Attribute Disentanglement for Generalized Zero-Shot Learning

Yoojin An, Sangyeon Kim, Yuxuan Liang, Roger Zimmermann, Dongho Kim, and Jihie Kim

IEEE Access 2022

IJCNN’22

Time-Aware Neighbor Sampling on Temporal Graphs

Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, and Bryan Hooi

In 2022 International Joint Conference on Neural Networks (IJCNN), 2022

@inproceedings{wang2022time,
  title = {Time-Aware Neighbor Sampling on Temporal Graphs},
  author = {Wang, Yiwei and Cai, Yujun and Liang, Yuxuan and Ding, Henghui and Wang, Changhu and Hooi, Bryan},
  booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
  pages = {1--8},
  year = {2022},
  organization = {IEEE}
}

NAACL’22

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, and Bryan Hooi

In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

@inproceedings{wang-etal-2022-rely,
  title = {Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis},
  author = {Wang, Yiwei and Chen, Muhao and Zhou, Wenxuan and Cai, Yujun and Liang, Yuxuan and Liu, Dayiheng and Yang, Baosong and Liu, Juncheng and Hooi, Bryan},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2022},
  publisher = {Association for Computational Linguistics},
  pages = {3071--3081}
}

2021

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 86

@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}
}

IJCAI’21

Modeling Trajectories with Neural Ordinary Differential Equations.

Yuxuan Liang, Kun Ouyang, Hanshu Yan, Yiwei Wang, Zekun Tong, and Roger Zimmermann

In International Joint Conference on Artificial Intelligence, 2021

@inproceedings{liang2021modeling,
  title = {Modeling Trajectories with Neural Ordinary Differential Equations.},
  author = {Liang, Yuxuan and Ouyang, Kun and Yan, Hanshu and Wang, Yiwei and Tong, Zekun and Zimmermann, Roger},
  booktitle = {International Joint Conference on Artificial Intelligence},
  pages = {1498--1504},
  year = {2021}
}

WWW’21

AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph

Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, and Yu Zheng

In Proceedings of the Web Conference, 2021

@inproceedings{pan2021autostg,
  title = {AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph},
  author = {Pan, Zheyi and Ke, Songyu and Yang, Xiaodu and Liang, Yuxuan and Yu, Yong and Zhang, Junbo and Zheng, Yu},
  booktitle = {Proceedings of the Web Conference},
  pages = {1846--1855},
  year = {2021}
}

WWW’21

Mixup for Node and Graph Classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

In Proceedings of the Web Conference, 2021

@inproceedings{wang2021mixup,
  title = {Mixup for Node and Graph Classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  booktitle = {Proceedings of the Web Conference},
  pages = {3663--3674},
  year = {2021}
}

WWW’21

Curgraph: Curriculum learning for graph classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

In Proceedings of the Web Conference, 2021

@inproceedings{wang2021curgraph,
  title = {Curgraph: Curriculum learning for graph classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  booktitle = {Proceedings of the Web Conference},
  pages = {1238--1248},
  year = {2021}
}

NeurIPS’21

Directed Graph Contrastive Learning

Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang

Advances in Neural Information Processing Systems 2021

@article{tong2021directed,
  title = {Directed Graph Contrastive Learning},
  author = {Tong, Zekun and Liang, Yuxuan and Ding, Henghui and Dai, Yongxing and Li, Xinke and Wang, Changhu},
  journal = {Advances in Neural Information Processing Systems},
  volume = {34},
  pages = {19580--19593},
  year = {2021}
}

NeurIPS’21

Adaptive Data Augmentation on Temporal Graphs

Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, and Bryan Hooi

Advances in Neural Information Processing Systems 2021

@article{wang2021adaptive,
  title = {Adaptive Data Augmentation on Temporal Graphs},
  author = {Wang, Yiwei and Cai, Yujun and Liang, Yuxuan and Ding, Henghui and Wang, Changhu and Bhatia, Siddharth and Hooi, Bryan},
  journal = {Advances in Neural Information Processing Systems},
  volume = {34},
  pages = {1440--1452},
  year = {2021}
}

MM’21

Learning Multi-context Aware Location Representations from Large-scale Geotagged Images

Yifang Yin, Ying Zhang, Zhenguang Liu, Yuxuan Liang, Sheng Wang, Rajiv Ratn Shah, and Roger Zimmermann

In Proceedings of the 29th ACM International Conference on Multimedia, 2021

@inproceedings{yin2021learning,
  title = {Learning Multi-context Aware Location Representations from Large-scale Geotagged Images},
  author = {Yin, Yifang and Zhang, Ying and Liu, Zhenguang and Liang, Yuxuan and Wang, Sheng and Shah, Rajiv Ratn and Zimmermann, Roger},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  pages = {899--907},
  year = {2021}
}

Visual Cascade Analytics of Large-Scale Spatiotemporal Data

Zikun Deng, Di Weng, Yuxuan Liang, Jie Bao, Yu Zheng, Tobias Schreck, Mingliang Xu, and Yingcai Wu

IEEE Transactions on Visualization and Computer Graphics 2021

@article{deng2021visual,
  title = {Visual Cascade Analytics of Large-Scale Spatiotemporal Data},
  author = {Deng, Zikun and Weng, Di and Liang, Yuxuan and Bao, Jie and Zheng, Yu and Schreck, Tobias and Xu, Mingliang and Wu, Yingcai},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {28},
  number = {6},
  pages = {2486--2499},
  year = {2021},
  publisher = {IEEE}
}

Structure-Aware Label Smoothing for Graph Neural Networks

Yiwei Wang, Yujun Cai, Yuxuan Liang, Wei Wang, Henghui Ding, Muhao Chen, Jing Tang, and Bryan Hooi

arXiv preprint arXiv:2112.00499 2021

@article{wang2021structure,
  title = {Structure-Aware Label Smoothing for Graph Neural Networks},
  author = {Wang, Yiwei and Cai, Yujun and Liang, Yuxuan and Wang, Wei and Ding, Henghui and Chen, Muhao and Tang, Jing and Hooi, Bryan},
  journal = {arXiv preprint arXiv:2112.00499},
  year = {2021}
}

2020

KDD’20

Nodeaug: Semi-Supervised Node Classification with Data Augmentation

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, and Bryan Hooi

In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020

PDF

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

@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}
}

ECML-PKDD’20

Revisiting convolutional neural networks for citywide crowd flow analytics

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, and David S Rosenblum

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2020

@inproceedings{liang2020revisiting,
  title = {Revisiting convolutional neural networks for citywide crowd flow analytics},
  author = {Liang, Yuxuan and Ouyang, Kun and Wang, Yiwei and Liu, Ye and Zhang, Junbo and Zheng, Yu and Rosenblum, David S},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages = {578--594},
  year = {2020},
  organization = {Springer}
}

KDD’20

Autost: Efficient Neural Architecture Search for Spatio-Temporal Prediction

Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, and Yu Zheng

In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020

@inproceedings{li2020autost,
  title = {Autost: Efficient Neural Architecture Search for Spatio-Temporal Prediction},
  author = {Li, Ting and Zhang, Junbo and Bao, Kainan and Liang, Yuxuan and Li, Yexin and Zheng, Yu},
  booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages = {794--802},
  year = {2020}
}

Fine-grained Urban Flow Inference

Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, David Rosenblum, and Yu Zheng

IEEE transactions on knowledge and data engineering 2020

@article{ouyang2020fine,
  title = {Fine-grained Urban Flow Inference},
  author = {Ouyang, Kun and Liang, Yuxuan and Liu, Ye and Tong, Zekun and Ruan, Sijie and Rosenblum, David and Zheng, Yu},
  journal = {IEEE transactions on knowledge and data engineering},
  year = {2020},
  publisher = {IEEE}
}

Predicting Citywide Crowd Flows in Irregular Regions using Multi-View Graph Convolutional Networks

Junkai Sun, Junbo Zhang, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, and Yu Zheng

IEEE Transactions on Knowledge and Data Engineering 2020

@article{sun2020predicting,
  title = {Predicting Citywide Crowd Flows in Irregular Regions using Multi-View Graph Convolutional Networks},
  author = {Sun, Junkai and Zhang, Junbo and Li, Qiaofei and Yi, Xiuwen and Liang, Yuxuan and Zheng, Yu},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2020},
  publisher = {IEEE}
}

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, and Yu Zheng

IEEE Transactions on Knowledge and Data Engineering 2020

@article{pan2020spatio,
  title = {Spatio-Temporal Meta Learning for Urban Traffic Prediction},
  author = {Pan, Zheyi and Zhang, Wentao and Liang, Yuxuan and Zhang, Weinan and Yu, Yong and Zhang, Junbo and Zheng, Yu},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2020},
  publisher = {IEEE}
}

Predicting Urban Water Quality with Ubiquitous Data - a Data-Driven Approach

Ye Liu, Yuxuan Liang, Kun Ouyang, Shuming Liu, David Rosenblum, and Yu Zheng

IEEE Transactions on Big Data 2020

@article{liu2020predicting,
  title = {Predicting Urban Water Quality with Ubiquitous Data - a Data-Driven Approach},
  author = {Liu, Ye and Liang, Yuxuan and Ouyang, Kun and Liu, Shuming and Rosenblum, David and Zheng, Yu},
  journal = {IEEE Transactions on Big Data},
  year = {2020},
  publisher = {IEEE}
}

UBICOMP’20

Dynamic Public Resource Allocation based on Human Mobility Prediction

Sijie Ruan, Jie Bao, Yuxuan Liang, Ruiyuan Li, Tianfu He, Chuishi Meng, Yanhua Li, Yingcai Wu, and Yu Zheng

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 2020

@article{ruan2020dynamic,
  title = {Dynamic Public Resource Allocation based on Human Mobility Prediction},
  author = {Ruan, Sijie and Bao, Jie and Liang, Yuxuan and Li, Ruiyuan and He, Tianfu and Meng, Chuishi and Li, Yanhua and Wu, Yingcai and Zheng, Yu},
  journal = {Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies},
  volume = {4},
  number = {1},
  pages = {1--22},
  year = {2020},
  publisher = {ACM New York, NY, USA}
}

AAAI’20

Learning to Generate Maps from Trajectories

Sijie Ruan, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, and Yu Zheng

In Proceedings of the AAAI conference on artificial intelligence, 2020

@inproceedings{ruan2020learning,
  author = {Ruan, Sijie and Long, Cheng and Bao, Jie and Li, Chunyang and Yu, Zisheng and Li, Ruiyuan and Liang, Yuxuan and He, Tianfu and Zheng, Yu},
  booktitle = {Proceedings of the AAAI conference on artificial intelligence},
  volume = {34},
  number = {01},
  pages = {890--897},
  year = {2020}
}

ECML-PKDD’20

Progressive Supervision for Node Classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2020

@inproceedings{wang2020progressive,
  title = {Progressive Supervision for Node Classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages = {266--281},
  year = {2020},
  organization = {Springer}
}

Federated Forest

Yang Liu, Yingting Liu, Zhijie Liu, Yuxuan Liang, Chuishi Meng, Junbo Zhang, and Yu Zheng

IEEE Transactions on Big Data 2020

@article{liu2020federated,
  title = {Federated Forest},
  author = {Liu, Yang and Liu, Yingting and Liu, Zhijie and Liang, Yuxuan and Meng, Chuishi and Zhang, Junbo and Zheng, Yu},
  journal = {IEEE Transactions on Big Data},
  year = {2020},
  publisher = {IEEE}
}

IJCNN’20

Unsupervised Learning of Disentangled Location Embeddings

Kun Ouyang, Yuxuan Liang, Ye Liu, David S Rosenblum, and Wenzhuo Yang

In 2020 International Joint Conference on Neural Networks (IJCNN), 2020

@inproceedings{ouyang2020unsupervised,
  title = {Unsupervised Learning of Disentangled Location Embeddings},
  author = {Ouyang, Kun and Liang, Yuxuan and Liu, Ye and Rosenblum, David S and Yang, Wenzhuo},
  booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)},
  pages = {1--8},
  year = {2020},
  organization = {IEEE}
}

Graphcrop: Subgraph cropping for graph classification

Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi

arXiv preprint arXiv:2009.10564 2020

@article{wang2020graphcrop,
  title = {Graphcrop: Subgraph cropping for graph classification},
  author = {Wang, Yiwei and Wang, Wei and Liang, Yuxuan and Cai, Yujun and Hooi, Bryan},
  journal = {arXiv preprint arXiv:2009.10564},
  year = {2020}
}

2019

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 140

@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}
}

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 580

@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}
}

IJCAI’19

Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking.

Nirandika Wanigasekara, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph Jay Williams, and David S Rosenblum

In International Joint Conference on Artificial Intelligence, 2019

@inproceedings{wanigasekara2019learning,
  title = {Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking.},
  author = {Wanigasekara, Nirandika and Liang, Yuxuan and Goh, Siong Thye and Liu, Ye and Williams, Joseph Jay and Rosenblum, David S},
  booktitle = {International Joint Conference on Artificial Intelligence},
  pages = {3835--3841},
  year = {2019}
}

2018

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 571

@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}
}

2017

SIGSPATIAL’17

Inferring Traffic Cascading Patterns

Yuxuan Liang, Zhongyuan Jiang, and Yu Zheng

In Proceedings of the 25th acm sigspatial international conference on advances in geographic information systems, 2017

@inproceedings{liang2017inferring,
  title = {Inferring Traffic Cascading Patterns},
  author = {Liang, Yuxuan and Jiang, Zhongyuan and Zheng, Yu},
  booktitle = {Proceedings of the 25th acm sigspatial international conference on advances in geographic information systems},
  pages = {1--10},
  year = {2017}
}

2016

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

@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}
}