Yongqiang Chen

Postdoctoral Researcher

CMU CLeaR Group

Email: yqchen24 [at] gmail (dot) com

I am an postdoctoral researcher at CMU-CLeaR group with Prof. Kun Zhang. Previously, I defended my Ph.D. in CSE at CUHK in 2024, working with Prof. James Cheng, and Prof. Bo Han at TMLR group. During my research journey, I also had wonderful time at RIKEN AIP, Tencent AI Lab, and Microsoft Research Asia.

My research focuses on developing new foundations of machine learning with causality, that promotes multiple desirable properties such as alignment, generalization, and interpretability of modern machine learning systems, empowering industrial applications and scientific practice with AI.

My PhD thesis made some early attempts on this problem. Meanwhile, I am always open for collaborations and communications. Our teams are also recruiting Research Assistants, Mphil and PhD students. The positions are available at multiple institutes. If you are interested in working with me, please feel free to drop me an email.

Selected Preprints and Publications

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

  • Discovery of the Hidden World with Large Language Models

    Chenxi Liu*, Yongqiang Chen*, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

    In Advances in Neural Information Processing Systems (NeurIPS), 2024.
    [paper] [project page] [code] [zh-cn blog]

  • A Sober Look at the Robustness of CLIPs to Spurious Features

    Qizhou Wang*, Yong Lin*, Yongqiang Chen*, Ludwig Schmidt, Bo Han, Tong Zhang

    In Advances in Neural Information Processing Systems (NeurIPS), 2024.
    [paper] [project page]

  • 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]

  • 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]

Selected Awards

  • Top Reviewer in NeurIPS (both tracks) 2024

  • ICML Travel Award 2024

  • NeurIPS Scholar Award 2023

  • Top Reviewer in NeurIPS 2022, 2023

  • Outstanding Reviewer in ICML 2022

  • Outstanding Undergraduate Thesis 2020

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

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

Academic Service

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

  • Other Conference referee: ICDM 2024, 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

  • Session Chair: NeurIPS'22 Session Chair

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