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Dong-Dong Wu

Ph.D. Student
The University of Tokyo



About Me

I am a first-year Ph.D. student at the University of Tokyo, supervised by Prof. Masashi Sugiyama. I am also a Junior Research Associate at the Imperfect Information Learning Team, RIKEN AIP. Before that, I received my M.Eng. degree from Southeast University under the supervision of Prof. Min-Ling Zhang. During my research journey, I also had wonderful time at MBZUAI, Ant Group and CASIA.

Research Interests

My research interests include Weakly Supervised Learning, Data-efficient Learning, AI Safety and Uncertainty in Large Model. Here’s my CV. If you are interested in discussing with me, welcome and feel free to contact me at dongdongwu1230 [at] gmail.com.

Publications

  1. Learning Robust Diffusion Models from Imprecise Supervision
    Dong-Dong Wu, Jiacheng Cui, Wei Wang, Zhiqiang Shen, Masashi Sugiyama
    PDF
    Submitted

  2. Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
    Wei Wang*, Dong-Dong Wu*, Ming Li, Jingxiong Zhang, Gang Niu, Masashi Sugiyama
    PDF
    Submitted

  3. Unleashing the Potential of Data Augmentation in Deep Partial Label Learning
    Dong-Dong Wu, Zhaoyi Li, Xiang Li, Zhiqiang Shen
    Submitted

  4. Dissimilarity-Driven Contrastive Learning for Robust Hashing in Partial Label Image Retrieval
    Zhiqiang Kou, Yucheng Xie, Dong-Dong Wu, Yuheng Jia, Jing Wang, Min-Ling Zhang, Xin Geng
    Submitted

  5. A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1
    Zhaoyi Li, Xiaohan Zhao, Dong-Dong Wu, Jiacheng Cui, Zhiqiang Shen
    The 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
    PDF Code Project Page
    Also accepted in ICML 2025 Workshop on Reliable and Responsible Foundation Models

  6. PLENCH: Realistic Evaluation of Deep Partial-Label Learning Algorithms
    Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
    Proceedings of the 13th International Conference on Learning Representations (ICLR), 2025
    PDF Code
    Spotlight

  7. Efficient Model Stealing Defense with Noise Transition Matrix
    Dong-Dong Wu, Chilin Fu, Weichang Wu, Wenwen Xia, Xiaolu Zhang, Jun Zhou, Min-Ling Zhang
    Proceedings of the 35th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
    PDF Code

  8. Robust Representation Learning for Unreliable Partial Label Learning
    Yu Shi*, Dong-Dong Wu*, Xin Geng, Min-Ling Zhang
    arXiv preprint, 2024
    PDF Code

  9. Distilling Reliable Knowledge for Instance-dependent Partial Label Learning
    Dong-Dong Wu*, Deng-Bao Wang*, Min-Ling Zhang
    Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI), 2024
    PDF Code

  10. Revisiting consistency regularization for deep partial label learning
    Dong-Dong Wu*, Deng-Bao Wang*, Min-Ling Zhang
    Proceedings of the 39th International Conference on Machine Learning (ICML), 2022
    PDF Code

  11. A new classification method based on the negation of a basic probability assignment in the evidence theory
    Dongdong Wu, Zijing Liu, Yongchuan Tang
    Engineering Applications of Artificial Intelligence (EAAI), 2020, 96: 103985
    PDF

  12. A new approach for generation of generalized basic probability assignment in the evidence theory
    Yongchuan Tang, Dongdong Wu, Zijing Liu
    Pattern Analysis and Applications (PAA), 2021, 24(3): 1007-1023
    PDF
    ESI Highly Cited Paper (Top 1%)

  13. An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory
    Dongdong Wu, Yongchuan Tang
    Quality and Reliability Engineering International (QRE), 2020; 36(5): 1786–1807
    PDF
    ESI Highly Cited Paper (Top 1%), Certificate

Experiences

Services

Program Committee Member:

2026(AAAI, PAKDD), 2025(IJCAI, ADMA, ECAI)

Conference Reviewer:

2026(ICLR, CVPR, AAAI, PAKDD, WACV), 2025(ICML, NeurIPS, CVPR, ICCV, IJCAI, ADMA, ECAI), 2024(CVPR, IJCAI, KDD), 2023(ECML-PKDD), 2022(ICML)

Talks

Awards

Competitions:

Honors:

Scholarships:


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