Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. - Qimai Li; Zhichao Han 003; Xiao-Ming Wu 003 (AAAI, 2018)

Large Margin Few-Shot Learning. - Yong Wang 032; Xiao-Ming Wu 003; Qimai Li; Jiatao Gu; Wangmeng Xiang; Lei Zhang 006; Victor O. K. Li (CoRR, 2018)

Label Efficient Semi-Supervised Learning via Graph Filtering. - Qimai Li; Xiao-Ming Wu 003; Han Liu 008; Xiaotong Zhang 003; Zhichao Guan (CVPR, 2019)

Reconstructing Capsule Networks for Zero-shot Intent Classification. - Han Liu 008; Xiaotong Zhang 003; Lu Fan; Xuandi Fu; Qimai Li; Xiao-Ming Wu 003; Albert Y. S. Lam (EMNLP/IJCNLP (1), 2019)

Attributed Graph Clustering via Adaptive Graph Convolution. - Xiaotong Zhang 003; Han Liu 008; Qimai Li; Xiao-Ming Wu 003 (IJCAI, 2019)

Generalized Label Propagation Methods for Semi-Supervised Learning. - Qimai Li; Xiao-Ming Wu 003; Zhichao Guan (CoRR, 2019)

Attributed Graph Learning with 2-D Graph Convolution. - Qimai Li; Xiaotong Zhang 003; Han Liu 008; Xiao-Ming Wu 003 (CoRR, 2019)

Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning. - Han Liu 008; Xianchao Zhang 001; Xiaotong Zhang 003; Qimai Li; Xiao-Ming Wu 003 (CoRR, 2019)

Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification. - Guangfeng Yan; Lu Fan; Qimai Li; Han Liu 008; Xiaotong Zhang 003; Xiao-Ming Wu 003; Albert Y. S. Lam (ACL, 2020)

A Closer Look at the Training Strategy for Modern Meta-Learning. - Jiaxin Chen; Xiao-Ming Wu 003; Yanke Li; Qimai Li; Li-Ming Zhan; Fu-Lai Chung (NeurIPS, 2020)

Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs. - Qimai Li; Xiaotong Zhang 003; Han Liu 008; Quanyu Dai; Xiao-Ming Wu 003 (KDD, 2021)

RPC: Representative possible world based consistent clustering algorithm for uncertain data. - Han Liu 008; Xiaotong Zhang 003; Xianchao Zhang 001; Qimai Li; Xiao-Ming Wu 003 (Comput. Commun., 2021)

Modeling User Behavior with Graph Convolution for Personalized Product Search. - Lu Fan; Qimai Li; Bo Liu 049; Xiao-Ming Wu 003; Xiaotong Zhang 003; Fuyu Lv; Guli Lin; Sen Li 001; Taiwei Jin; Keping Yang (WWW, 2022)

Multi-Agent Path Finding via Tree LSTM. - Yuhao Jiang; Kunjie Zhang; Qimai Li; Jiaxin Chen; Xiaolong Zhu (CoRR, 2022)

Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks. - Quanyu Dai; Xiao-Ming Wu 003; Lu Fan; Qimai Li; Han Liu 008; Xiaotong Zhang 003; Dan Wang 002; Guli Lin; Keping Yang (Pattern Recognit., 2022)

Recon: Reducing Conflicting Gradients From the Root For Multi-Task Learning. - Guangyuan Shi; Qimai Li; Wenlong Zhang; Jiaxin Chen; Xiao-Ming Wu 003 (ICLR, 2023)

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors. - Han Liu 008; Xingshuo Huang; Xiaotong Zhang 003; Qimai Li; Fenglong Ma; Wei Wang 077; Hongyang Chen; Hong Yu 005; Xianchao Zhang 001 (IJCAI, 2023)

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. - Joseph Suarez; David Bloomin; Kyoung Whan Choe; Hao Xiang Li; Ryan Sullivan; Nishaanth Kanna; Daniel Scott; Rose S. Shuman; Herbie Bradley; Louis Castricato; Phillip Isola; Chenghui Yu; et al. (NeurIPS, 2023)

Simple yet Effective Gradient-Free Graph Convolutional Networks. - Yulin Zhu; Xing Ai; Qimai Li; Xiao-Ming Wu 003; Kai Zhou 001 (CoRR, 2023)

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. - 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; et al. (CoRR, 2023)

Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model. - Kai Yang; Jian Tao; Jiafei Lyu; Chunjiang Ge; Jiaxin Chen; Qimai Li; Weihan Shen; Xiaolong Zhu; Xiu Li 001 (CoRR, 2023)

Adaptive Graph Convolution Methods for Attributed Graph Clustering. - Xiaotong Zhang 003; Han Liu 008; Qimai Li; Xiao-Ming Wu 003; Xianchao Zhang 001 (IEEE Trans. Knowl. Data Eng., 2023)

Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning. - Joseph Suárez; Kyoung Whan Choe; David Bloomin; Jianming Gao; Yunkun Li; Yao Feng; Saidinesh Pola; Kun Zhang; Yonghui Zhu; Nikhil Pinnaparaju; Hao Xiang Li; Nishaanth Kanna; et al. (CoRR, 2025)