Yongqiang Chen

54 papers A* 18B 1Journal 34Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad. CoRR Yongqiang Chen, Chenxi Liu, Zhenhao Chen, Tongliang Liu, Bo Han, Kun Zhang
2026	J	`jnl`	On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents. CoRR Deyu Zou, Yongqiang Chen, Fan Feng, Mufei Li, Pan Li, Yu Gong, James Cheng
2026	J	`jnl`	ParamMem: Augmenting Language Agents with Parametric Reflective Memory. CoRR Tianjun Yao, Yongqiang Chen, Yujia Zheng, Pan Li, Zhiqiang Shen, Kun Zhang
2025	J	`jnl`	Can Large Language Models Help Experimental Design for Causal Discovery? CoRR Junyi Li, Yongqiang Chen, Chenxi Liu, Qianyi Cai, Tongliang Liu, Bo Han, Kun Zhang, Hui Xiong
2025	J	`jnl`	DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization. CoRR Jiaqi Wang, Yuhang Zhou, Zhixiong Zhang, Qiguang Chen, Yongqiang Chen, James Cheng
2025	J	`jnl`	DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization. Trans. Mach. Learn. Res. Jiaqi Wang, Yuhang Zhou, Zhixiong Zhang, Qiguang Chen, Yongqiang Chen, James Cheng
2025	J	`jnl`	Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment? CoRR Hongzheng Yang, Yongqiang Chen, Zeyu Qin, Tongliang Liu, Chaowei Xiao, Kun Zhang, Bo Han
2025	A*	`conf`	Eliciting Causal Abilities in Large Language Models for Reasoning Tasks. AAAI Yajing Wang, Zongwei Luo, Jingzhe Wang, Zhanke Zhou, Yongqiang Chen, Bo Han
2025	A*	`conf`	Hierarchical Graph Tokenization for Molecule-Language Alignment. ICML Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian
2025	A*	`conf`	Learning Graph Invariance by Harnessing Spuriosity. ICLR Tianjun Yao, Yongqiang Chen, Kai Hu, Tongliang Liu, Kun Zhang, Zhiqiang Shen
2025	A*	`conf`	MESH - Understanding Videos Like Human: Measuring Hallucinations in Large Video Models. ACM Multimedia Garry Yang, Zizhe Chen, Man Hon Wong, Haoyu Lei, Yongqiang Chen, Zhenguo Li, Kaiwen Zhou, James Cheng
2025	J	`jnl`	MESH - Understanding Videos Like Human: Measuring Hallucinations in Large Video Models. CoRR Garry Yang, Zizhe Chen, Man Hon Wong, Haoyu Lei, Yongqiang Chen, Zhenguo Li, Kaiwen Zhou, James Cheng
2025	J	`jnl`	On the Thinking-Language Modeling Gap in Large Language Models. CoRR Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang
2025	J	`jnl`	Pruning Spurious Subgraphs for Graph Out-of-Distribtuion Generalization. CoRR Tianjun Yao, Haoxuan Li, Yongqiang Chen, Tongliang Liu, Le Song, Eric P. Xing, Zhiqiang Shen
2025	—	`conf`	Retrieval-Augmented Generation with Hierarchical Knowledge. EMNLP (Findings) Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng
2025	J	`jnl`	Retrieval-Augmented Generation with Hierarchical Knowledge. CoRR Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng
2025	J	`jnl`	Towards Scalable Oversight with Collaborative Multi-Agent Debate in Error Detection. CoRR Yongqiang Chen, Gang Niu, James Cheng, Bo Han, Masashi Sugiyama
2025	J	`jnl`	Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation. CoRR Deyu Zou, Yongqiang Chen, Mufei Li, Siqi Miao, Chenxi Liu, Bo Han, James Cheng, Pan Li
2024	A*	`conf`	A Sober Look at the Robustness of CLIPs to Spurious Features. NeurIPS Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang
2024	J	`jnl`	Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration. CoRR Haoze Sun, Wenbo Li, Jiayue Liu, Kaiwen Zhou, Yongqiang Chen, Yong Guo, Yanwei Li, Renjing Pei, Long Peng, Yujiu Yang
2024	A*	`conf`	Discovery of the Hidden World with Large Language Models. NeurIPS Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
2024	J	`jnl`	Discovery of the Hidden World with Large Language Models. CoRR Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
2024	J	`jnl`	Do CLIPs Always Generalize Better than ImageNet Models? CoRR Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang
2024	J	`jnl`	Eliciting Causal Abilities in Large Language Models for Reasoning Tasks. CoRR Yajing Wang, Zongwei Luo, Jingzhe Wang, Zhanke Zhou, Yongqiang Chen, Bo Han
2024	A*	`conf`	Empowering Graph Invariance Learning with Deep Spurious Infomax. ICML Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang
2024	J	`jnl`	Empowering Graph Invariance Learning with Deep Spurious Infomax. CoRR Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang
2024	A*	`conf`	Enhancing Evolving Domain Generalization through Dynamic Latent Representations. AAAI Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng
2024	J	`jnl`	Enhancing Evolving Domain Generalization through Dynamic Latent Representations. CoRR Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng
2024	A*	`conf`	Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. ICLR Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng
2024	J	`jnl`	Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. CoRR Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng
2024	J	`jnl`	HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment. CoRR Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian
2024	A*	`conf`	HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection. NeurIPS Binghui Xie, Yixuan Wang, Yongqiang Chen, Kaiwen Zhou, Yu Li, Wei Meng, James Cheng
2024	A*	`conf`	How Interpretable Are Interpretable Graph Neural Networks? ICML Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
2024	J	`jnl`	How Interpretable Are Interpretable Graph Neural Networks? CoRR Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
2024	A*	`conf`	On the Comparison between Multi-modal and Single-modal Contrastive Learning. NeurIPS Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki
2024	J	`jnl`	On the Comparison between Multi-modal and Single-modal Contrastive Learning. CoRR Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki
2024	J	`jnl`	Solving the non-submodular network collapse problems via Decision Transformer. Neural Networks Kaili Ma, Han Yang, Shanchao Yang, Kangfei Zhao, Lanqing Li, Yongqiang Chen, Junzhou Huang, James Cheng, Yu Rong
2024	J	`jnl`	UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation. CoRR Juzheng Zhang, Yatao Bian, Yongqiang Chen, Quanming Yao
2023	J	`jnl`	Calibrating and Improving Graph Contrastive Learning. Trans. Mach. Learn. Res. Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng
2023	A*	`conf`	Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NeurIPS Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng
2023	J	`jnl`	Does Invariant Graph Learning via Environment Augmentation Learn Invariance? CoRR Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng
2023	A*	`conf`	Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. ICLR Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng
2023	J	`jnl`	Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. CoRR Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian, James Cheng
2023	J	`jnl`	Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
2023	J	`jnl`	Towards out-of-distribution generalizable predictions of chemical kinetics properties. CoRR Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong
2023	A*	`conf`	Understanding and Improving Feature Learning for Out-of-Distribution Generalization. NeurIPS Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
2022	A*	`conf`	Exact Shape Correspondence via 2D graph convolution. NeurIPS Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng
2022	J	`jnl`	Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
2022	A*	`conf`	Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
2022	J	`jnl`	Pareto Invariant Risk Minimization. CoRR Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng
2022	A*	`conf`	Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng
2022	J	`jnl`	Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng
2021	J	`jnl`	Improving Graph Representation Learning by Contrastive Regularization. CoRR Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng
2021	B	`conf`	Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs. IJCNN Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng