Yongqiang Chen

Publication Activity (10 Years)

Years Active: 2022-2024
Publications (10 Years): 17

Top Topics

Subset Selection

Positional Information

Risk Minimization

Invariant Representations

Top Venues

Publications

Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng
Enhancing Evolving Domain Generalization through Dynamic Latent Representations. AAAI (2024)
Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian
HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment. CoRR (2024)
Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
How Interpretable Are Interpretable Graph Neural Networks? CoRR (2024)
Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang
Empowering Graph Invariance Learning with Deep Spurious Infomax. CoRR (2024)
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 (2024)
Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang
Do CLIPs Always Generalize Better than ImageNet Models? CoRR (2024)
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)
Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? CoRR (2023)
Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR (2023)
Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng
Calibrating and Improving Graph Contrastive Learning. Trans. Mach. Learn. Res. 2023 (2023)
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 (2023)
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 (2023)
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)
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)
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)
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng
Pareto Invariant Risk Minimization. CoRR (2022)
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)