My current research focuses on examining the theoretical foundations of decentralized learning (or swarm learning). I am also dedicated to utilizing elegant theoretical insights to construct fast and generalizable decentralized learning algorithms. Please refer to my publicatios below.


  1. ICML 2023 (My Favorite)
    Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
    Tongtian ZhuFengxiang HeKaixuan Chen, Mingli Song, and Dacheng Tao
    In Proceedings of the 40th International Conference on Machine Learning, 23–29 jul 2023
  2. AAAI 2023 (Oral)
    Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
    Shunyu LiuYihe ZhouJie SongTongya ZhengKaixuan ChenTongtian ZhuZunlei Feng, and Mingli Song
    In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 23–29 jul 2023
  3. KDD 2023
    Improving Expressivity of GNNs with Subgraph-Specific Factor Embedded Normalization
    Kaixuan ChenShunyu LiuTongtian Zhu, Ji Qiao, Yun Su, Yingjie Tian, Tongya Zheng, Haofei Zhang, and 3 more authors
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 23–29 jul 2023
  4. ECAI 2023
    Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket
    Yuwen Wang, Shunyu LiuKaixuan ChenTongtian Zhu, Ji Qiao, Mengjie Shi, Yuanyu Wan, and Mingli Song
    In European Conference on Artificial Intelligence, 30 sep–04 oct 2023


  1. ICML 2022 (Spotlight)
    Topology-aware Generalization of Decentralized SGD
    Tongtian ZhuFengxiang HeLan ZhangZhengyang Niu, Mingli Song, and Dacheng Tao
    In Proceedings of the 39th International Conference on Machine Learning, 17–23 jul 2022