| Qwen2. 5 Technical Report A Yang, B Yang, B Zhang, B Hui, B Zheng, B Yu, C Li, D Liu, F Huang, ... arXiv preprint arXiv:2412.15115, 2024 | 1031 | 2024 |
| Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching Z Li, J Lu, S Luo, D Zhu, Y Shao, Y Li, Z Zhang, Y Wang, C Wu IEEE Transactions on Big Data, 2022 | 44 | 2022 |
| CFlowNets: Continuous Control with Generative Flow Networks Y Li, S Luo, H Wang, J Hao International Conference on Learning Representations (ICLR), 2023, 2023 | 20 | 2023 |
| Mining Latent Relationships among Clients: Peer-to-peer Federated Learning with Adaptive Neighbor Matching Z Li, J Lu, S Luo, D Zhu, Y Shao, Y Li, Z Zhang, C Wu arXiv preprint arXiv:2203.12285, 2022 | 16 | 2022 |
| Ensemble Federated Adversarial Training with Non-IID data S Luo, D Zhu, Z Li, C Wu IJCAI International Workshop on Federated and Transfer Learning (FTL-IJCAI'21), 2021 | 8 | 2021 |
| Transfer heterogeneous knowledge among peer-to-peer teammates: A model distillation approach Z Xue, S Luo, C Wu, P Zhou, K Bian, W Du arXiv preprint arXiv:2002.02202, 2020 | 7 | 2020 |
| GFlowNets with Human Feedback Y Li, S Luo, Y Shao, J Hao International Conference on Learning Representations (ICLR) Tiny Paper, 2023, 2023 | 5 | 2023 |
| S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? S Luo, Y Li, J Li, K Kuang, F Liu, Y Shao, C Wu ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022, 2022 | 5 | 2022 |
| Multi-agent continuous control with Generative Flow Networks S Luo, Y Li, S Liu, X Zhang, Y Shao, C Wu Neural Networks (NN), 2024 | 3 | 2024 |
| Utilizing RBC system for taxation policy evaluation: An adaptive interaction framework based on deep reinforcement learning S Luo, S Liu, T Cai, C Wu Expert Systems with Applications, 126365, 2025 | | 2025 |
| 面向复杂社会的多智能体仿真建模 吴超， 罗双 中国人工智能学会通讯, 8-10, 2021 | | 2021 |