Yongqiang Chen @ CUHK

Selected Preprints and Publications

Email me if you have any questions about my papers or code. * indicates equal contributions.

Interpretable and Generalizable Graph Neural Networks via Subgraph Multilinear Extension
Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
In ICLR workshop Machine Learning for Genomics Explorations (MLGenX), 2024. Spotlight presentation.
[paper]
Discovery of the Hidden World with Large Language Models
Chenxi Liu*, Yongqiang Chen*, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, and Kun Zhang
In arXiv 2402.03941, 2024.
[paper] [project page]
Do CLIPs Always Generalize Better than ImageNet Models?
Qizhou Wang*, Yong Lin*, Yongqiang Chen*, Ludwig Schmidt, Bo Han, Tong Zhang
In arXiv 2403.11497, 2024.
[paper] [project page]
Enhancing Evolving Domain Generalization through Dynamic Latent Representations
Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng
In Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024. Oral presentation (2.2%).
[paper] [code]
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Yongqiang Chen*, Wei Huang*, Kaiwen Zhou*, Yatao Bian, Bo Han, James Cheng
In Advances in Neural Information Processing Systems (NeurIPS), 2023.
Also presented in ICLR Workshop on Domain Generalization (ICLR DG) Spotlight presentation.
[paper] [code] [slides]
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng
In Advances in Neural Information Processing Systems (NeurIPS), 2023.
Also presented in ICLR Workshop on Domain Generalization (ICLR DG) Spotlight presentation.
[paper] [code] [slides]
Towards Out-of-Distribution Generalizable Predictions of Chemical Kinetics Properties
Zihao Wang*, Yongqiang Chen*, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong
In NeurIPS Workshop on AI for Science Oral presentation, 2023.
[paper] [code]
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in OOD Generalization
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Peilin Zhao, Bo Han, James Cheng and others.
In International Conference on Learning Representations (ICLR), 2023. Oral presentation at ICLR DG.
Presented at ICML PODS, 2022, with Kaiwen Zhou*.
[paper] [code] [talk] [slides] [Wilds Leaderborad] [zh-cn blog]
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
In Advances in Neural Information Processing Systems (NeurIPS), 2022. Spotlight presentation (5.2%).
Presented at ICML SCIS 2022. 🔥SOTA results in GOOD Benchmark🔥.
[paper] [workshop ver.] [code] [talk] [slides] [zh-cn blog]
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng
In International Conference on Learning Representations (ICLR), 2022.
[paper] [code] [talk] [slides]

Yongqiang Chen

Selected Preprints and Publications

Talks

Selected Awards

Academic Service