Yonggang Zhang (PostDoc at HKBU)


Publications


I research trustworthy machine learning and reasoning. In the following, represents the corresponding author, and * represents equal contribution.

[Selected Conference Papers, Selected Journal Articles]


Conference Papers (Selected)

  1. W. Zhang, C. Wan, Y. Zhang, Y.M. Cheung, X. Tian, X. Shen, J. Ye.
    Interpreting and Improving Large Language Models in Arithmetic Calculation.
    In International Conference on Machine Learning (ICML 2024) (Oral), Published Online, 2024 .
    [ Link ] [ CODE ]

  2. Y. Zhang, Z. Yang, X. Tian, N. Wang, T. Liu, B. Han.
    Robust Training of Federated Models with Extremely Label Deficiency.
    In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 .
    [ Link ] [ CODE ]

  3. P. Zheng*, Y. Zhang*, Z. Fang, T. Liu, D. Lian, B. Han.
    Beyond Linear Spherical Interpolation: Noise Correction for Image Interpolation with Diffusion Models.
    In International Conference on Learning Representations (ICLR 2024) (Spotlight), Published Online, 2024 .
    [ Link ] [ CODE ]

  4. J. Nie, Y. Zhang, Z. Fang, T. Liu, B. Han, X. Tian.
    Out-of-Distribution Detection with Negative Prompts.
    In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 .
    [ Link ] [ CODE ]

  5. R. Dai, Y. Zhang, A. Li, T. Liu, X. Yang, B. Han.
    Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.
    In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 .
    [ Link ] [ CODE ]

  6. Z. Tang, Y. Zhang, S.Shi, X. Tian, T. Liu, B. Han, X. Chu.
    FedImpro: Measuring and Improving Client Update in Federated Learning.
    In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 .
    [ Link ] [ CODE ]

  7. Z. Yang*, Y. Zhang*, Y. Zheng, X. Tian, H. Peng, T. Liu, B. Han.
    FedFed: Feature Distillation against Data Heterogeneity in Federated.
    In Conference on Neural Information Processing Systems (NeurIPS 2023), Published Online, 2023 .
    [ Link ] [ CODE ]

  8. M. Yang, Z. Fang, Y. Zhang, Y. Du, F. Liu, J.F Ton, J. Wang.
    Invariant Learning via Probability of Sufficient and Necessary Causes.
    In Conference on Neural Information Processing Systems (NeurIPS 2023) (Spotlight), Published Online, 2023 .
    [ Link ] [ CODE ] [ Spotlight ]

  9. R. Dai, Y. Zhang, Z. Fang, B. Han, X. Tian.
    Moderately Distributional Exploration for Domain Generalization.
    In International Conference on Machine Learning (ICML 2023), Published Online, 2023 .
    [ Link ] [ CODE ]

  10. C. Sun, Y. Zhang, W. Chaoqun, Q. Wang, Y. Li, T. Liu, B. Han, X. Tian.
    Towards Lightweight Black-Box Attacks against Deep Neural Networks.
    In Conference on Neural Information Processing Systems (NeurIPS 2022), Published Online, 2022 .
    [ Link ] [ CODE ]

  11. Y. Chen, Y. Zhang, H. Yang, K. Ma, B. Xie, T. Liu, B. Han, J. Cheng.
    Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.
    In Conference on Neural Information Processing Systems (NeurIPS 2022), Published Online, 2022 .
    [ Link ] [ CODE ] [ Spotlight ]

  12. Z. Tang*, Y. Zhang*, S. Shi, X. He, B. Han, X. Chu.
    Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
    In International Conference on Machine Learning (ICML 2022), Published Online, 2022 .
    [ arXiv ] [ CODE ]

  13. Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, K. Zhang.
    CausalAdv: Adversarial Robustness Through the Lens of Causality.
    In International Conference on Learning Representations (ICLR 2022), Published Online, 2022 .
    [ Link ] [ CODE ]

  14. Y. Zhang, Y. Li, T. Liu, X. Tian.
    Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks.
    In International Conference on Machine Learning (ICML 2020), Published Online, 2020 .
    [ Link ]


Published Journal Articles (Selected)

  1. J. Nie, Y. Luo, S. Ye, Y. Zhang, X. Tian, Z. Fang.
    Out-of-Distribution Detection with Virtual Outlier Smoothing.
    International Journal of Computer Vision, 2024.
    [
    Link ]

  2. Y. Zhang, X. Tian, Y. Li, X. Wang, D. Tao.
    Principal Component Adversarial Example.
    IEEE Transactions on Image Processing, Accepted, 2020 (ERA&CORE A*).
    [ Link ]