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Selected Publications

(* indicates equal contributions; † indicates corresponding authors)

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

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

3. 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 (TNNLS 2023), [PDF] [Code].

4. 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].

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

6. 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.
   36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), [PDF] [Code] [Poster].

7. 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].

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

9. 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].

10. Towards Adversarially Robust 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].

11. 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].

12. 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].

13. 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].

14. 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].

15. 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].

16. 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), [PDF] [Code] [Poster].

17. 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].

18. 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].

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Professional Service

Organizer or Chair

Workshop Chair of 2024 IEEE Conference On Artificial Intelligence (IEEE CAI 2024)
Tutorial Chair of AJCAI Australasian Joint Conference on Artificial Intelligence 2023
Track Chair of the 12th International Symposium on Information and Communication Technology (SOICT 2023)
Workshop Chair of the 1st International Conference on AI-generated Content (AIGC 2023)
Organizer of ACML 2022 Workshop on Weakly Supervised Representation Learning
Organizer of ACML 2021 Workshop on Weakly Supervised Representation Learning
Organizer of TrustML Young Scientist Seminars (TrustML YSS) [Video series]

Associate Editor

IEEE Transactions on Artificial Intelligence

Area Chair/Senior Program Member

AJCAI 2023

Reviewer for Conferences

ICLR 2021-2023, ICML 2020- 2023, NeurIPS 2021-2023, CVPR 2022-2023, etc

Reviewer for Journals

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

Lecturer

2023 S2 Semester @School of Computer Science, the University of Auckland, Advanced Topics in Machine Learning (COMPSCI 760)

Guest Lecturer

2022 Spring Semester (S1 Term) @Graduate School of Information Science and Technology, The University of Tokyo, Special Topics in Mechano-Informatics II (4850-1022)---Trustworthy machine learning [course slides]
2023 Spring Semester (S1 Term) @Graduate School of Information Science and Technology, The University of Tokyo, Special Topics in Mechano-Informatics II (4850-1022)---Trustworthy machine learning [course slides]

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 Thesis Examiner

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

[07/2023 - Present] Zihao Luo Master Student@UoA
[07/2023 - Present] Yihao (Justin) Wu Master Student@UoA (with Assoc. Prof. Yun Sun Koh)

Research Advisor

(Students who closely work or worked with me.)
[07/2023 -Present] Di Zhao PhD student@UoA (with Assoc. 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)

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Funding

RIKEN-Kyushu Univ Science & Technology Hub Collaborative Research Program, Japan [FY2022]

JSPS Grants-in-Aid for Scientific Research (KAKENHI), Early-Career Scientists, Grant Number 22K17955, Japan [FY2022 - FY2023]

JST Strategic Basic Research Programs, ACT-X, Grant Number JPMJAX21AF, Japan [Annoucement] [FY2021 - FY2023]