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)

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)

Does Invariant Graph Learning via Environment Augmentation Learn Invariance? - Yongqiang Chen 002; Yatao Bian; Kaiwen Zhou; Binghui Xie; Bo Han 003; James Cheng (NeurIPS, 2023)

Understanding and Improving Feature Learning for Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou; Yatao Bian; Bo Han 003; James Cheng (NeurIPS, 2023)

Towards Understanding Feature Learning in Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou; Yatao Bian; Bo Han 003; James Cheng (CoRR, 2023)

Towards out-of-distribution generalizable predictions of chemical kinetics properties. - Zihao Wang 001; Yongqiang Chen 002; Yang Duan; Weijiang Li; Bo Han 003; James Cheng; Hanghang Tong (CoRR, 2023)

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. - Yongqiang Chen 002; Binghui Xie; Kaiwen Zhou; Bo Han 003; Yatao Bian; 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)

Enhancing Evolving Domain Generalization through Dynamic Latent Representations. - Binghui Xie; Yongqiang Chen 002; Jiaqi Wang 012; Kaiwen Zhou; Bo Han 003; Wei Meng 001; James Cheng (AAAI, 2024)

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. - Binghui Xie; Yatao Bian; Kaiwen Zhou; Yongqiang Chen 002; Peilin Zhao; Bo Han 003; Wei Meng 001; James Cheng (ICLR, 2024)

How Interpretable Are Interpretable Graph Neural Networks? - Yongqiang Chen 002; Yatao Bian; Bo Han 003; James Cheng (ICML, 2024)

Empowering Graph Invariance Learning with Deep Spurious Infomax. - Tianjun Yao; Yongqiang Chen 002; Zhenhao Chen; Kai Hu 010; Zhiqiang Shen; Kun Zhang 001 (ICML, 2024)

Discovery of the Hidden World with Large Language Models. - Chenxi Liu; Yongqiang Chen 002; Tongliang Liu; Mingming Gong; James Cheng; Bo Han 003; Kun Zhang 001 (NeurIPS, 2024)

A Sober Look at the Robustness of CLIPs to Spurious Features. - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (NeurIPS, 2024)

HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection. - Binghui Xie; Yixuan Wang; Yongqiang Chen 002; Kaiwen Zhou; Yu Li; Wei Meng 001; James Cheng (NeurIPS, 2024)

Do CLIPs Always Generalize Better than ImageNet Models? - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (CoRR, 2024)

HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment. - Yongqiang Chen 002; Quanming Yao; Juzheng Zhang; James Cheng; Yatao Bian (CoRR, 2024)

UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation. - Juzheng Zhang; Yatao Bian; Yongqiang Chen 002; Quanming Yao (CoRR, 2024)

Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration. - Haoze Sun; Wenbo Li 002; Jiayue Liu; Kaiwen Zhou; Yongqiang Chen 002; Yong Guo; Yanwei Li; Renjing Pei; Long Peng 003; Yujiu Yang (CoRR, 2024)

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)

Learning Graph Invariance by Harnessing Spuriosity. - Tianjun Yao; Yongqiang Chen 002; Kai Hu 010; Tongliang Liu; Kun Zhang 001; Zhiqiang Shen (ICLR, 2025)

DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization. - Jiaqi Wang 012; Yuhang Zhou; Zhixiong Zhang; Qiguang Chen; Yongqiang Chen 002; James Cheng (Trans. Mach. Learn. Res., 2025)

Can Large Language Models Help Experimental Design for Causal Discovery? - Junyi Li; Yongqiang Chen 002; Chenxi Liu; Qianyi Cai; Tongliang Liu; Bo Han 003; Kun Zhang 001; Hui Xiong 001 (CoRR, 2025)

Retrieval-Augmented Generation with Hierarchical Knowledge. - Haoyu Huang; Yongfeng Huang; Junjie Yang; Zhenyu Pan; Yongqiang Chen 002; Kaili Ma 001; Hongzhi Chen; James Cheng (CoRR, 2025)

On the Thinking-Language Modeling Gap in Large Language Models. - Chenxi Liu; Yongqiang Chen 002; Tongliang Liu; James Cheng; Bo Han 003; Kun Zhang 001 (CoRR, 2025)

Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment? - Hongzheng Yang; Yongqiang Chen 002; Zeyu Qin; Tongliang Liu; Chaowei Xiao; Kun Zhang 001; Bo Han 003 (CoRR, 2025)

Pruning Spurious Subgraphs for Graph Out-of-Distribtuion Generalization. - Tianjun Yao; Haoxuan Li 001; Yongqiang Chen 002; Tongliang Liu; Le Song; Eric P. Xing; Zhiqiang Shen (CoRR, 2025)