Jingfeng Zhang


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Jingfeng Zhang

Jingfeng Zhang (张 景锋)


Lecturer (a.k.a. Tenured Assistant Professor) @the University of Auckland, School of Computer Science  

Visiting Research Scientist @RIKEN Center for Advanced Intelligence Project
 

[Google Scholar] [Github]
E-mail: jingfeng.zhang9660@gmail.com / jingfeng.zhang@auckland.ac.nz

I am PhD/Doctoral Accredited Supervisor.
I am seeking motivated individuals who are interested in pursuing a PhD or research master's degree at the University of Auckland, New Zealand.


Education & Experiences

2021 - 2023, Postdoctoral Researcher → Research Scientist @ Imperfect Information Learning Team
RIKEN Center for Advanced Intelligence Project
Supervised by Prof. Masashi Sugiyama

2016 - 2020, Doctor of Philosophy @ School of Computing
National University of Singapore
Supervised by Prof. Mohan Kankanhalli

2012 - 2016, Bachelor of Engineering @ Taishan College
Shandong University


News

  • May. 2024: 2 papers have been accepted at ICML 2024! Congratulations to Kunda, Sen, Wuerkaixi and Jiacheng!

  • Apr. 2024: My research "Towards Robust Foundation Models: Adversarial Contrastive Learning" is online at ICLR BlogPosts 2024.

  • Jan. 2024: 3 papers have been accepted at ICLR 2024! Congratulations to Xilie, Keyi, Wuerkaixi and Sen!

  • Nov. 2023: I will travel to New Orleans, US, to attend NeurIPS 2023 from December 10th to December 17th, 2023. See you all there!

  • Sept. 2023: Two papers were accepted at NeurIPS 2023! Congratulations to Xilie!

  • July 2023: I am attending ICML 2023. See you all in Hawaii!

  • March 2023: I am invited to give an onsite talk at the EPFL-CIS and RIKEN-AIP Joint Workshop on Machine Learning and Artificial Intelligence 2023.

  • Feb 2023: I am invited to give an onsite talk at the Rising Stars in AI Symposium 2023 at KAUST.

See more news here.


Research

    I am machine learning researcher. My long-term goal is to develop safe, trustworthy, reliable, and extensible machine learning (ML) technologies.
    My current interest is developing trustworthy machine learning theories and algorithms, designing & developing robust foundation models, and adapting the foundation models reliably to downstream applications.


Selected Publications

(* indicates equal contributions; † indicates corresponding authors)
    1. Balancing Similarity and Complementarity for Unimodal and Multimodal Federated Learning.
      K. Yan, S. Cui, A. Wuerkaixi, J. Zhang, B. Han, G. Niu, M. Sugiyama, C. Zhang
      The 41st International Conference on Machine Learning (ICML 2024), [PDF] [Code][Poster].

    2. Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training.
      J. Zhang, F. Liu, D. Zhou, J. Zhang, T. Liu.
      The 41st International Conference on Machine Learning (ICML 2024), [PDF] [Code][Poster].

    3. AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework.
      X. Xu, J. Zhang†, M. Kankanhalli.
      The 12th International Conference on Learning Representations (ICLR 2024), [PDF] [Code][Poster].

    4. An LLM can Fool Itself: A Prompt-Based Adversarial Attack.
      X. Xu*, K. Kong*, N. Liu, L. Cui, D. Wang, J. Zhang†, M. Kankanhalli.
      The 12th International Conference on Learning Representations (ICLR 2024), [PDF] [Code][Poster].

    5. Accurate Forgetting for Heterogeneous Federated Continual Learning.
      A. Wuerkaixi*, S. Cui*, J. Zhang*, K. Yan, B. Han, G. Niu, L. Fang, C. Zhang, M. Sugiyama.
      The 12th International Conference on Learning Representations (ICLR 2024), [PDF] [Code][Poster].

    6. BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning.
      J. Zhang*†, B. Song*, H. Wang, B. Han, T. Liu, L. Liu, M. Sugiyama.
      IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI 2024), [PDF] [Code].

    7. Learning A Robust Foundation Model Against Clean-label Data Poisoning Attacks at Downstream Tasks.
      T. Zhou*, H. Yan*, B. Han, L. Liu†, J. Zhang†.
      Neural Networks (NN 2024), [PDF] [Code].

    8. Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection.
      X. Xu*, J. Zhang*†, F. Liu, M. Sugiyama, M. Kankanhalli.
      The 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023, Spotlight, Top 3.06%), [PDF] [Code] [Poster].

    9. Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization.
      X. Xu*, J. Zhang*†, F. Liu, M. Sugiyama, M. Kankanhalli.
      The 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), [PDF] [Code] [Poster].

    10. On the Effectiveness of Adversarial Training Against Backdoor Attacks.
      Y. Gao*, D. Wu*, J. Zhang†, G. Gan, S. Xia†, G. Niu, M. Sugiyama.
      IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS 2023), [PDF] [Code][Poster].

    11. GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks.
      S. Ghamizi, J. Zhang†, M. Cordy, M. Papadakis, M. Sugiyama, Y. Le Traon.
      The 40th International Conference on Machine Learning (ICML 2023), [PDF] [Code] [Poster].

    12. Synergy-of-Experts: Collaborate to Improve Adversarial Robustness.
      S. Cui*, J. Zhang*, J. Liang, B. Han, M. Sugiyama, C. Zhang.
      The 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), [PDF] [Code] [Poster].

    13. Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks.
      J. Zhou*, J. Zhu*, J. Zhang, T. Liu, G. Niu, B. Han, M. Sugiyama.
      The 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), [PDF] [Code] [Poster].

    14. NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels.
      J. Zhang*, X. Xu*, B. Han, T. Liu, N. Gang, L. Cui, M. Sugiyama.
      Transactions on Machine Learning Research (TMLR 2022), [PDF] [Code].

    15. Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.
      X. Peng*, F. Liu*, J. Zhang, L. Lan, J. Ye, T. Liu, B. Han.
      The 28th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2022), [PDF] [Code] [Poster].

    16. Adversarial Attacks and Defense For Non-parametric Two Sample Tests.
      X. Xu*, J. Zhang*†, F. Liu, M. Sugiyama, and M. Kankanhalli.
      The 39th International Conference on Machine Learning (ICML 2022), [PDF] [Code] [Poster].

    17. Towards Adversarially Robust Deep Image Denoising.
      H. Yan, J. Zhang, J. Feng, M. Sugiyama, and V. Y. F. Tan.
      The 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), [PDF] [Code] [Poster].

    18. Decision Boundary-aware Data Augmentation for Adversarial Training.
      C. Chen*, J. Zhang*, X. Xu, L. Lyu, C. Chen, T. Hu, G. Chen.
      IEEE Transactions on Dependable and Secure Computing (IEEE TDSC 2022), [PDF] [Code].

    19. Reliable Adversarial Distillation with Unreliable Teachers.
      J. Zhu, J. Yao, B. Han, J. Zhang, T. Liu, G. Niu, J. Zhou, J. Xu, H. Yang.
      In Proceedings of 10th International Conference on Learning Representations (ICLR 2022), [PDF] [Code] [Poster].

    20. CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection.
      H. Yan, J. Zhang†, G. Niu, J. Feng, V. Y. F. Tan, M. Sugiyama.
      In Proceedings of 38th International Conference on Machine Learning (ICML 2021), [PDF] [Code] [Poster].

    21. Maximum Mean Discrepancy is Aware of Adversarial Attacks.
      R. Gao*, F. Liu*, J. Zhang*, B. Han, T. Liu, G. Niu, and M. Sugiyama.
      In Proceedings of 38th International Conference on Machine Learning (ICML 2021), [PDF] [ Code] [Poster].

    22. Learning Diverse-Structured Networks for Adversarial Robustness.
      X. Du*, J. Zhang*, B. Han, T. Liu, Y. Rong, G. Niu, J. Huang and M. Sugiyama.
      In Proceedings of 38th International Conference on Machine Learning (ICML 2021), [PDF] [ Code] [Poster].

    23. Geometry-aware Instance-reweighted Adversarial Training.
      J. Zhang, J. Zhu, G. Niu, B. Han, M. Sugiyama, and M. Kankanhalli.
      In Proceedings of 9th International Conference on Learning Representations (ICLR 2021, Oral, Top 1.77%), [PDF] [Code] [Poster].

    24. Attacks Which Do Not Kill Training Make Adversarial Learning Stronger.
      J. Zhang*, X. Xu*, B. Han, G. Niu, L. Cui, M. Sugiyama, and M. Kankanhalli.
      In Proceedings of 37th International Conference on Machine Learning (ICML 2020), [PDF] [Code] [Poster].

    25. Towards Robust ResNet: A Small Step but A Giant Leap.
      J. Zhang, B. Han, L. Wynter, B. Low, and M. Kankanhalli.
      In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), [PDF] [Code] [Poster].


Professional Service

    Reviewer for Conferences

    ICLR 2021-2024, ICML 2020-2024, NeurIPS 2021-2024, CVPR 2022-2024, etc

    Reviewer for Journals

    MLJ, TMLR, TNNLS, TPMAI, PR, Neural Networks, etc.

    Teaching Assistant

    2017/2018 Semester 2, BT5152 @School of Computing, National University of Singapore, Decision Making Technologies
    2017/2018 Semester 2, CS3243 @School of Computing, National University of Singapore, Introduction to Artificial Intelligence
    2016/2017 Semester 2, CS3243 @School of Computing, National University of Singapore, Introduction to Artificial Intelligence

    PhD/Master Thesis Examiner

    Dr. Cheng Wen @The University of Sydney, Permutation-invariant Representation Learning for 3D Point Cloud Processing, in 2024
    Dr. Ziqi Zhang @Tsinghua University, Medical Image Classification under Data Deficiency Scenarios, in 2023
    Mr. Chengbin Du @The University of Sydney, Gradient-based Automatic Attack of Text-to-Image Model, in 2023
    Dr. Yipeng KANG @Tsinghua University, Emergence and Transition of Language in Cooperative Multi-Agent Systems, in 2023
    Dr. Liang ZENG @Tsinghua University, Deep Representation Learning on Graph-Structured Data with Applications, in 2023
    Dr. Salah GHAMIZI @University of Luxembourg, Multi-objective Robust Machine Learning For Critical Systems With Scarce Data, in 2022

    Research Supervisor

    (Students whom I take duties to supervise)
    [07/2023 - Present] Mr. Zihao LUO Master Student@UoA
    [03/2024 - Present] Ms. Xin CHEN PhD Student@UoA (with Prof. Gill Dobbie)

    Research Advisor

    (Students who closely work or worked with me)
    [07/2023 -Present] Di Zhao PhD student@UoA (with Prof. Yun Sun Koh and Prof. Gill Dobbie)
    [04/2019 - Present] Xilie Xu Undergrad@SDU → PhD Student@NUS (with Prof. Mohan Kankanhalli)

    [03/2020 - 12/2022] Jianing Zhu Undergrad@SCU → PhD Student@BUHK (with Dr. Bo Han)
    [09/2020 - 12/2022] Hanshu Yan PhD Student@NUS (with Assoc. Prof. Vincent Tan and Dr. Jiashi Feng) → Research Scientist@ByteDance, SG
    [04/2021 - 06/2023] Bo Song Master Student@SDU (with Prof. Lei Liu)
    [04/2021 - 06/2023] Ting Zhou Master Student@SDU (with Prof. Lei Liu and Dr. Hanshu Yan)
    [07/2020 – 3/2021] Xuefeng Du (now PhD Student@UW-Madison) RA@BUHK (with Dr. Bo Han)
    [07/2020 – 3/2021] Ruize Gao (now PhD Student@NUS) RA@BUHK (with Dr. Bo Han)


Funding

    New Staff Research Grant, the University of Auckland, New Zealand [FY2024 - FY2025]
    RIKEN-Kyushu Univ Science & Technology Hub Collaborative Research Program, Japan [FY2022]