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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 interests lie in weakly supervised learning (for classification, generation, and LLM post-training), efficient learning (for agents, LLMs, and data), and AI safety. Here’s my CV. If you are interested in discussing with me, welcome and feel free to contact me at dongdongwu1230 [at] gmail.com.

News

  • [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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