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ORCIDYongqiang Chen 0002
Person information
- affiliation: The Chinese University of Hong Kong
Other persons with the same name
- Yongqiang Chen — disambiguation page
- Yongqiang Chen 0001 — Chinese Academy of Science, Beijing, China
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2020 – today
- 2024
[j2]Kaili Ma, Han Yang
, Shanchao Yang, Kangfei Zhao, Lanqing Li, Yongqiang Chen, Junzhou Huang, James Cheng, Yu Rong:
Solving the non-submodular network collapse problems via Decision Transformer. Neural Networks 176: 106328 (2024)- [c11]Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng:
Enhancing Evolving Domain Generalization through Dynamic Latent Representations. AAAI 2024: 16040-16048 - [c10]Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng:
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. ICLR 2024 - [c9]Yongqiang Chen, Yatao Bian, Bo Han, James Cheng:
How Interpretable Are Interpretable Graph Neural Networks? ICML 2024 - [c8]Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang:
Empowering Graph Invariance Learning with Deep Spurious Infomax. ICML 2024 - [i16]Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng:
Enhancing Evolving Domain Generalization through Dynamic Latent Representations. CoRR abs/2401.08464 (2024) - [i15]Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng:
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. CoRR abs/2402.03139 (2024) - [i14]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. CoRR abs/2402.03941 (2024) - [i13]Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang:
Do CLIPs Always Generalize Better than ImageNet Models? CoRR abs/2403.11497 (2024) - [i12]Yongqiang Chen, Yatao Bian, Bo Han, James Cheng:
How Interpretable Are Interpretable Graph Neural Networks? CoRR abs/2406.07955 (2024) - [i11]Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian:
HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment. CoRR abs/2406.14021 (2024) - [i10]Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang:
Empowering Graph Invariance Learning with Deep Spurious Infomax. CoRR abs/2407.11083 (2024) - [i9]Juzheng Zhang, Yatao Bian, Yongqiang Chen, Quanming Yao:
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation. CoRR abs/2408.00863 (2024) - 2023
- [j1]Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng:
Calibrating and Improving Graph Contrastive Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng:
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. ICLR 2023 - [c6]Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng:
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NeurIPS 2023 - [c5]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Understanding and Improving Feature Learning for Out-of-Distribution Generalization. NeurIPS 2023 - [i8]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR abs/2304.11327 (2023) - [i7]Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong:
Towards out-of-distribution generalizable predictions of chemical kinetics properties. CoRR abs/2310.03152 (2023) - [i6]Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng:
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? CoRR abs/2310.19035 (2023) - [i5]Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian, James Cheng:
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. CoRR abs/2311.18194 (2023) - 2022
- [c4]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c3]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c2]Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng:
Exact Shape Correspondence via 2D graph convolution. NeurIPS 2022 - [i4]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i3]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i2]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng:
Pareto Invariant Risk Minimization. CoRR abs/2206.07766 (2022) - 2021
- [c1]Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng:
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs. IJCNN 2021: 1-8 - [i1]Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng:
Improving Graph Representation Learning by Contrastive Regularization. CoRR abs/2101.11525 (2021)
Coauthor Index
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