| A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection Q Li, Z Wen, Z Wu, S Hu, N Wang, Y Li, X Liu, B He IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021 | 1256 | 2021 |
| When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting? X Liu, Y Liang, C Huang, Y Zheng, B Hooi, R Zimmermann SIGSPATIAL 2022, 2022 | 79* | 2022 |
| LSTM Multi-Modal UNet for Brain Tumor Segmentation F Xu, H Ma, J Sun, R Wu, X Liu, Y Kong ICIVC 2019, 2019 | 77 | 2019 |
| LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting X Liu, Y Xia, Y Liang, J Hu, Y Wang, L Bai, C Huang, Z Liu, B Hooi, ... NeurIPS 2023, 2023 | 65 | 2023 |
| The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems S Hu, Y Li, X Liu, Q Li, Z Wu, B He ACM Transactions on Intelligent Systems and Technology (TIST), 2022 | 60 | 2022 |
| UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting X Liu, J Hu, Y Li, S Diao, Y Liang, B Hooi, R Zimmermann WWW 2024, 2024 | 50 | 2024 |
| Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment Y Xia, Y Liang, H Wen, X Liu, K Wang, Z Zhou, R Zimmermann NeurIPS 2023, 2023 | 40 | 2023 |
| TrajFormer: Efficient Trajectory Classification with Transformers Y Liang, K Ouyang, Y Wang, X Liu, H Chen, J Zhang, Y Zheng, ... CIKM 2022, 2022 | 37 | 2022 |
| Reinventing Node-centric Traffic Forecasting for Improved Accuracy and Efficiency X Liu, Y Liang, C Huang, H Hu, Y Cao, B Hooi, R Zimmermann ECML-PKDD 2024, 2024 | 22* | 2024 |
| Deep Active Learning for Computer Vision: Past and Future R Takezoe, X Liu, S Mao, MT Chen, Z Feng, S Zhang, X Wang APSIPA Transactions on Signal and Information Processing, 2023 | 18 | 2023 |
| LLMs for Relational Reasoning: How Far are We? Z Li, Y Cao, X Xu, J Jiang, X Liu, YS Teo, S Lin, Y Liu ICSE LLM4Code Workshop 2024, 2024 | 15 | 2024 |
| OD Morphing: Balancing Simplicity with Faithfulness for OD Bundling Y Lyu, X Liu, H Chen, A Mangal, K Liu, C Chen, B Lim IEEE Transactions on Visualization and Computer Graphics (TVCG), 2019 | 14 | 2019 |
| Towards unifying diffusion models for probabilistic spatio-temporal graph learning J Hu, X Liu, Z Fan, Y Liang, R Zimmermann SIGSPATIAL 2024, 2024 | 7 | 2024 |
| Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting Q Liu, X Liu, C Liu, Q Wen, Y Liang NeurIPS 2024, 2024 | 3 | 2024 |
| A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading Y Li, B Luo, Q Wang, N Chen, X Liu, B He EMNLP 2024, 2024 | 2 | 2024 |
| Low-rank Adaptation for Spatio-Temporal Forecasting W Ruan, W Chen, X Dang, J Zhou, W Li, X Liu, Y Liang arXiv preprint arXiv:2404.07919, 2024 | 2 | 2024 |
| Improving Neural Logic Machines via Failure Reflection Z Li, Y Cao, Y Zheng, X Liu, B Wu, T Li, X Xu, J Jiang, YS Teo, SW Lin, ... Forty-first International Conference on Machine Learning, 2024 | 1 | 2024 |
| Prompt-Enhanced Spatio-Temporal Graph Transfer Learning J Hu, X Liu, Z Fan, Y Yin, S Xiang, S Ramasamy, R Zimmermann CIKM 2024, 2024 | 1 | 2024 |
| GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation T Aksu, G Woo, J Liu, X Liu, C Liu, S Savarese, C Xiong, D Sahoo arXiv preprint arXiv:2410.10393, 2024 | | 2024 |
| Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts X Liu, J Liu, G Woo, T Aksu, Y Liang, R Zimmermann, C Liu, S Savarese, ... arXiv preprint arXiv:2410.10469, 2024 | | 2024 |
