Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning. - Xinyan Dai; Xiao Yan 002; Kaiwen Zhou; Han Yang 002; Kelvin Kai Wing Ng; James Cheng; Yu Fan (CoRR, 2019)

Convolutional Embedding for Edit Distance. - Xinyan Dai; Xiao Yan 002; Kaiwen Zhou; Yuxuan Wang; Han Yang 002; James Cheng (SIGIR, 2020)

Edit Distance Embedding using Convolutional Neural Networks. - Xinyan Dai; Xiao Yan 002; Kaiwen Zhou; Yuxuan Wang; Han Yang 002; James Cheng (CoRR, 2020)

Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs. - Han Yang 002; Xiao Yan 002; Xinyan Dai; James Cheng (CoRR, 2020)

Rethinking Graph Regularization for Graph Neural Networks. - Han Yang 002; Kaili Ma 001; James Cheng (AAAI, 2021)

Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs. - Han Yang 002; Xiao Yan 002; Xinyan Dai; Yongqiang Chen 002; James Cheng (IJCNN, 2021)

Improving Graph Representation Learning by Contrastive Regularization. - Kaili Ma 001; Haochen Yang; Han Yang 002; Tatiana Jin; Pengfei Chen; Yongqiang Chen 002; Barakeel Fanseu Kamhoua; James Cheng (CoRR, 2021)

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. - Yongqiang Chen 002; Han Yang 002; Yonggang Zhang 003; Kaili Ma 001; Tongliang Liu; Bo Han 003; James Cheng (ICLR, 2022)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Yatao Bian; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (NeurIPS, 2022)

Exact Shape Correspondence via 2D graph convolution. - Barakeel Fanseu Kamhoua; Lin Zhang; Yongqiang Chen 002; Han Yang 002; Kaili Ma 001; Bo Han 003; Bo Li 001; James Cheng (NeurIPS, 2022)

HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization. - Yuntao Gui; Yidi Wu; Han Yang 002; Tatiana Jin; Boyang Li 016; Qihui Zhou; James Cheng; Fan Yu (SC, 2022)

Invariance Principle Meets Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (CoRR, 2022)

Pareto Invariant Risk Minimization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Kaili Ma 001; Yonggang Zhang 003; Han Yang 002; Bo Han 003; James Cheng (CoRR, 2022)

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Bingzhe Wu; Yonggang Zhang 003; Kaili Ma 001; Han Yang 002; Peilin Zhao; Bo Han 003; James Cheng (ICLR, 2023)

SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification. - Yuntao Gui; Xiao Yan 002; Peiqi Yin; Han Yang 002; James Cheng (CoRR, 2023)

Calibrating and Improving Graph Contrastive Learning. - Kaili Ma 001; Garry Yang; Han Yang 002; Yongqiang Chen 002; James Cheng (Trans. Mach. Learn. Res., 2023)

Solving the non-submodular network collapse problems via Decision Transformer. - Kaili Ma 001; Han Yang 002; Shanchao Yang; Kangfei Zhao; Lanqing Li; Yongqiang Chen 002; Junzhou Huang; James Cheng; Yu Rong 001 (Neural Networks, 2024)