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Dong-Dong Wu

Ph.D. Student
The University of Tokyo


About Me

I am a second-year Ph.D. student at the University of Tokyo, where I am fortunate to be supervised by Prof. Masashi Sugiyama. I am also a Junior Research Associate at the Imperfect Information Learning Team, RIKEN Center for Advanced Intelligence Project (RIKEN AIP). Previously, 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 focuses mainly on weakly supervised learning (for classification, generation, and LLM post-training), with broader interests in efficient learning (for agents, LLMs, and data) and AI safety. Here’s my CV. If you would like to discuss research, feel free to reach me at dongdongwu1230 [at] gmail.com.

News

  • [2026.05] Achieved Rank 1 in AVI Challenge 2026 (ACM MM 2026) – Track 2: Cognitive Ability Assessment. A paper about our method will be released to the ACM MM 2026 main track soon.
  • [2026.05] One paper accepted at ICML 2026.
  • [2026.03] Received the Travel Award for ICLR 2026.
  • [2026.01] Two papers accepted at ICLR 2026 (one main conference, one workshop).

Selected Publications  [View all publications]

  1. Making Partial-Label Datasets Easier: A Simple Yet Highly Effective Data Augmentation for Deep Partial-Label Learning
    Dong-Dong Wu, Zhaoyi Li, Xiang Li, Zhiqiang Shen
    The 5th DataCV Workshop and Challenge with CVPR2026

  2. DELTA: Robustly Training Diffusion Models with Weak Annotations
    Dong-Dong Wu, Jiacheng Cui, Wei Wang, Zhiqiang Shen, Masashi Sugiyama
    ICLR 2026 Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy

  3. Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
    Wei Wang*, Dong-Dong Wu*, Ming Li, Jingxiong Zhang, Gang Niu, Masashi Sugiyama
    Proceedings of the 14th International Conference on Learning Representations (ICLR), 2026

  4. 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

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

  6. 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

  7. 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

Experiences

Services

Program Committee Member:

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

Conference Reviewer:

2026(ICLR, ICML, CVPR, ECCV, IJCAI, AAAI, AAAI-AIBSD, PAKDD, WACV, BMVC), 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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