Yongqiang Chen

Ph.D. Candidate

Department of Computer Science and Engineering

The Chinese University of Hong Kong

Email: yqchen [at] cse (dot) cuhk (dot) edu (dot) hk

Email / Github / Publications / LinkedIn

I am currently a Ph.D. candidate in CSE department at CUHK, advised by Prof. James Cheng. Previously, I obtained my B.Eng. degree from HIT, and enjoyed one year wonderful internship at Microsoft Research Asia.

My research spans causal learning and reasoning for the new theoretical and practical foundations of modern machine learning, with a focus on problems/data from industrial and scientific practice.

I am always open for possible collaborations, and visiting opportunities, please do not hesitate to contact me if you are interested! Please also consider giving me feedback via the anoymouns form : )

Selected Preprints and Publications

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

  • Learning Causality for Modern Machine Learning

    Ph.D. Thesis, CUHK.
    [slides]

  • HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment

    Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, and Yatao Bian

    In ICML 2024 Workshop on Foundation Models in the Wild, 2024.
    [paper] [project page]

  • How Interpretable Are Interpretable Graph Neural Networks?

    Yongqiang Chen, Yatao Bian, Bo Han, James Cheng

    In International Conference on Machine Learning (ICML), 2024.
    Also appeared In ICLR MLGenX workshop, 2024. Spotlight presentation.
    [paper] [code]

  • 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]

Talks

  • DataFun Causal Inference Summit -- Graph and Causality, Virtual, October 2023.

  • GBA Young Scientist Gathering, Virtual, May 2023.

  • AI Time PhD Workshop on ICLR 2023, Virtual, Mar 2023.

  • Invited Talk to Causality Seminar, Virtual, December 2022.

  • AI Time PhD Workshop on NeurIPS 2022, Virtual, November 2022.

  • Invited Talk to Prof. Yiping Ke's Group at NTU, Virtual, June 2022.

  • AI Time PhD Workshop on ICLR 2022, Virtual, June 2022.

Selected Awards

  • NeurIPS Scholar Award 2023

  • Top Reviewer in NeurIPS 2022, 2023

  • Outstanding Reviewer in ICML 2022

  • Outstanding Undergraduate Thesis 2020

  • Zuguang Ma Scholarship (< 0.1%) 2019

  • National Scholarship (twice, 1%) 2018, 2019

  • Bronze Medal, The 2018 ACM-ICPC Asia-East Continent Final 2018

Academic Service

  • Chair: NeurIPS'22 Session chair

  • ML Conference referee: ICML 2022/23/24, NeurIPS 2022/23/24 & DB Track 2022/23/24, ICLR 2024, AISTATS 2024, UAI 2024, AAAI 2024/25, IJCAI 2024, LoG 2023/24, CLeaR 2024

  • Other Conference referee: CVPR 2024, SIGKDD 2022, VLDB 2022

  • Journal referee: T-PAMI, TMLR, TKDD, Neural Networks, TNNLS, Pattern Recognition

  • Workshop referee: ICML SCIS 2023, NeurIPS CRL 2023, ICLR DG 2023

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