Xu Liu

According to our database, Xu Liu authored at least 25 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models.

Xu Liu, Taha Aksu, Juncheng Liu, Qingsong Wen, Yuxuan Liang, Caiming Xiong, Silvio Savarese, Doyen Sahoo, Junnan Li, Chenghao Liu

CoRR, March, 2025

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, Kai Zheng

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

2024

Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts.

Xu Liu, Juncheng Liu, Gerald Woo, Taha Aksu, Yuxuan Liang, Roger Zimmermann, Chenghao Liu, Silvio Savarese, Caiming Xiong, Doyen Sahoo

CoRR, 2024

GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation.

Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, Doyen Sahoo

CoRR, 2024

A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading.

Yuan Li, Bingqiao Luo, Qian Wang, Nuo Chen, Xu Liu, Bingsheng He

CoRR, 2024

Prompt-Enhanced Spatio-Temporal Graph Transfer Learning.

Junfeng Hu, Xu Liu, Zhencheng Fan, Yifang Yin, Shili Xiang, Savitha Ramasamy, Roger Zimmermann

CoRR, 2024

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

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

Proceedings of the ACM on Web Conference 2024, 2024

User Behavior Enriched Temporal Knowledge Graphs for Sequential Recommendation.

Hengchang Hu, Wei Guo, Xu Liu, Yong Liu, Ruiming Tang, Rui Zhang, Min-Yen Kan

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Reinventing Node-centric Traffic Forecasting for Improved Accuracy and Efficiency.

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

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

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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

LLMs for Relational Reasoning: How Far are We?

Zhiming Li, Yushi Cao, Xiufeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-Wei Lin, Yang Liu

LLM4CODE@ICSE, 2024

Improving Neural Logic Machines via Failure Reflection.

Zhiming Li, Yushi Cao, Yan Zheng, Xu Liu, Bozhi Wu, Tianlin Li, Xiufeng Xu, Junzhe Jiang, Yon Shin Teo, Shang-Wei Lin, Yang Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning.

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

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

CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading.

Yuan Li, Bingqiao Luo, Qian Wang, Nuo Chen, Xu Liu, Bingsheng He

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Prompt-Based Spatio-Temporal Graph Transfer Learning.

Junfeng Hu, Xu Liu, Zhencheng Fan, Yifang Yin, Shili Xiang, Savitha Ramasamy, Roger Zimmermann

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He

IEEE Trans. Knowl. Data Eng., April, 2023

Do We Really Need Graph Neural Networks for Traffic Forecasting?

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

CoRR, 2023

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

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems.

Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He

ACM Trans. Intell. Syst. Technol., 2022

When do contrastive learning signals help spatio-temporal graph forecasting?

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

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

TrajFormer: Efficient Trajectory Classification with Transformers.

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

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

2021

Spatio-Temporal Graph Contrastive Learning.

Xu Liu, Yuxuan Liang, Yu Zheng, Bryan Hooi, Roger Zimmermann

CoRR, 2021

2020

OD Morphing: Balancing Simplicity with Faithfulness for OD Bundling.

Yan Lyu, Xu Liu, Hanyi Chen, Arpan Mangal, Kai Liu, Chao Chen, Brian Y. Lim

IEEE Trans. Vis. Comput. Graph., 2020

2019

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Xu Liu, Bingsheng He

CoRR, 2019