Publications
Kai Yang, Jian Tao, Jiafei Lyu, Chunjiang Ge, Jiaxin Chen, Qimai Li, Weihan Shen, Xiaolong Zhu, Xiu Li. “Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model.” In submission. 2023. [HuggingFace Daily Ppaer]
Joseph Suárez, Phillip Isola, Kyoung Whan Choe, David Bloomin, Hao Xiang Li, Nikhil Pinnaparaju, Nishaanth Kanna, Daniel Scott, Ryan Sullivan, Rose S. Shuman, Lucas de Alcântara, Herbie Bradley, Louis Castricato, Kirsty You, Yuhao Jiang, Qimai Li, Jiaxin Chen, Xiaolong Zhu. “Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning.” In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems. 2023. [NeurIPS-23]
Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wei Wang, Hongyang Chen, Hong Yu, Xianchao Zhang. “Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors.” In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. 2023. [IJCAI-23]
Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu, Xianchao Zhang, “Adaptive Graph Convolution Methods for Attributed Graph Clustering,” in IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12384-12399, 1 Dec. 2023, doi: 10.1109/TKDE.2023.3278721. [TKDE]
Guangyuan Shi, Qimai Li, Wenlong Zhang, Jiaxin Chen, Xiao-Ming Wu. “Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning.” In Proceedings of the Eleventh International Conference on Learning Representations. 2023. [ICLR-23]
Yulin Zhu, Xing Ai, Qimai Li, Xiao-Ming Wu, Kai Zhou. “Simple yet Effective Gradient-Free Graph Convolutional Networks.” arXiv preprint arXiv:2302.00371. 2023.
Yuhao Jiang, Kunjie Zhang, Qimai Li, Jiaxin Chen, Xiaolong Zhu. “Multi-Agent Path Finding via Tree LSTM.” In AAAI 23 Multi Agent Path Finding workshop. 2023.
Qimai Li. “Learning on graphs with graph convolution.” [Ph.D. Thesis].
Lu Fan, Qimai Li, Bo Liu, Xiao-Ming Wu, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang. “Modeling user behavior with graph convolution for personalized product search.” In Proceedings of the ACM Web Conference 2022 (WWW ‘22). 2022.
Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai, Xiao-Ming Wu. “Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs.” In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ‘21). 2021. [KDD-21] [PDF] [CODE]
Han Liu, Xiaotong Zhang, Xianchao Zhang, Qimai Li, Xiao-Ming Wu. “RPC: Representative possible world based consistent clustering algorithm for uncertain dat”, Computer Communications, Volume 176, 2021, Pages 128-137, ISSN 0140-3664.
Jiaxin Chen, Xiao-Ming Wu, Yanke Li, Qimai Li, Li-Ming Zhan, Fu-lai Chung. “A Closer Look at the Training Strategy for Modern Meta-Learning.” in Proceedings of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS-20).
Guangfeng Yan, Lu Fan, Qimai Li (co-first author), Han Liu, Xiaotong Zhang, Xiao-Ming Wu, Albert Y.S. Lam. “Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Long Paper). 2020. [ACL-20] [PDF] [CODE]
Han Liu, Xiaotong Zhang, Lu Fan, Xuandi Fu, Qimai Li, Xiao-Ming Wu, Albert Y.S. Lam. “Reconstructing Capsule Networks for Zero-shot Intent Classification.” In Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing (Long Paper). 2019. [EMNLP-19] [PDF]
Xiaotong Zhang, Han Liu, Qimai Li (co-first author) and Xiao-Ming Wu. “Attributed Graph Clustering via Adaptive Graph Convolution.” In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019. [IJCAI-19] [PDF] [CODE] [PPT]
Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, and Zhichao Guan. “Label Efficient Semi-Supervised Learning via Graph Filtering.” In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. [CVPR-19] [PDF] [CODE] [PPT] [POSTER]
Yong Wang, Xiao-Ming Wu, Qimai Li, Jiatao Gu, Wangmeng Xiang, Lei Zhang, and Victor OK Li. “Large Margin Meta-Learning for Few-Shot Classification.” Workshop on Meta-Learning (MetaLearn 2018) at NeurIPS. 2018.
Qimai Li, Zhichao Han, and Xiao-Ming Wu. “Deeper insights into graph convolutional networks for semi-supervised learning.” Thirty-Second AAAI Conference on Artificial Intelligence. 2018. Selected as one of the Most Influential AAAI Papers by Paper Digest (2478 Citations as of December, 2023). [AAAI-18 Oral] [PDF] [CODE] [BLOG] [PPT]