Chen, Yongqiang

30 publications

ICLR 2026 On the Thinking-Language Modeling Gap in Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang

ICLR 2026 Reducing Belief Deviation in Reinforcement Learning for Active Reasoning of LLM Agents Deyu Zou, Yongqiang Chen, Jianxiang Wang, Garry Yang, Mufei Li, Qing Da, James Cheng, Pan Li, Yu Gong

ICLR 2026 Tracedet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models Shenxu Chang, Junchi Yu, Weixing Wang, Yongqiang Chen, Jialin Yu, Philip Torr, Jindong Gu

ICLR 2025 BrainOOD: Out-of-Distribution Generalizable Brain Network Analysis Jiaxing Xu, Yongqiang Chen, Xia Dong, Mengcheng Lan, Tiancheng Huang, Qingtian Bian, James Cheng, Yiping Ke

TMLR 2025 DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization Jiaqi Wang, Yuhang Zhou, Zhixiong Zhang, Qiguang Chen, Yongqiang Chen, James Cheng

AAAI 2025 Eliciting Causal Abilities in Large Language Models for Reasoning Tasks Yajing Wang, Zongwei Luo, Jingzhe Wang, Zhanke Zhou, Yongqiang Chen, Bo Han

ICML 2025 Hierarchical Graph Tokenization for Molecule-Language Alignment Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian

ICLR 2025 Learning Graph Invariance by Harnessing Spuriosity Tianjun Yao, Yongqiang Chen, Kai Hu, Tongliang Liu, Kun Zhang, Zhiqiang Shen

ICLRW 2025 On the Language of Thoughts in Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang

NeurIPS 2025 Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Tianjun Yao, Haoxuan Li, Yongqiang Chen, Tongliang Liu, Le Song, Eric P. Xing, Zhiqiang Shen

ICLRW 2025 Rich Feature Learning via Diversification Xi Leng, Yongqiang Chen, Xiaoying Tang, Yatao Bian

NeurIPS 2024 A Sober Look at the Robustness of CLIPs to Spurious Features Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang

NeurIPS 2024 Discovery of the Hidden World with Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

ICML 2024 Empowering Graph Invariance Learning with Deep Spurious Infomax Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang

AAAI 2024 Enhancing Evolving Domain Generalization Through Dynamic Latent Representations Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng

ICLR 2024 Enhancing Neural Subset Selection: Integrating Background Information into Set Representations Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng

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

ICML 2024 How Interpretable Are Interpretable Graph Neural Networks? Yongqiang Chen, Yatao Bian, Bo Han, James Cheng

ICMLW 2024 Improving Graph-Language Alignment with Hierarchical Graph Tokenization Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian

ICLRW 2024 Interpretable and Generalizable Graph Learning via Subgraph Multilinear Extension Yongqiang Chen, Yatao Bian, Bo Han, James Cheng

NeurIPS 2024 On the Comparison Between Multi-Modal and Single-Modal Contrastive Learning Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki

TMLR 2023 Calibrating and Improving Graph Contrastive Learning Ma Kaili, Garry Yang, Han Yang, Yongqiang Chen, James Cheng

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

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

NeurIPSW 2023 Towards Out-of-Distribution Generalizable Predictions of Chemical Kinetic Properties Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong

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

NeurIPS 2022 Exact Shape Correspondence via 2D Graph Convolution Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Ma Kaili, Bo Han, Bo Li, James Cheng

ICMLW 2022 Invariance Principle Meets Out-of-Distribution Generalization on Graphs Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

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

ICLR 2022 Understanding and Improving Graph Injection Attack by Promoting Unnoticeability Yongqiang Chen, Han Yang, Yonggang Zhang, Ma Kaili, Tongliang Liu, Bo Han, James Cheng