Wei Wang (王 維)  Ph.D. Student Sugiyama Lab Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8561, Japan Email: wwangwitsel [at] gmail.com OR wangw [at] g.ecc.u-tokyo.ac.jp              OR wei.wang [at] riken.jp

Biography

I am a 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 Center for Advanced Intelligence Project. I received a M.Eng. degree from Southeast University under the supervision of Prof. Min-Ling Zhang. I received a B.Eng. degree from Huazhong University of Science and Technology.

My research interests are theories and algorithms of machine learning. Most recently, I am interested in reliable machine learning and foundation models.

Feel free to email me if you are interested in talking with me.

Publication

• W. Wang, T. Ishida, Y.-J. Zhang, G. Niu, and M. Sugiyama.
  Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical.
  In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pp. 50683-50710, Vienna, Austria, 2024.
  [paper] [code]
• W. Wang, L. Feng, Y. Jiang, G. Niu, M.-L. Zhang, and M. Sugiyama.
  Binary Classification with Confidence Difference.
  In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pp. 5936-5960, New Orleans, LA, 2023.
  [paper] [code]
• J.-X. Mao, W. Wang, and M.-L. Zhang.
  Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps.
  In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), pp. 4055-4063, Macau, China, 2023.
  [paper] [code]
• W. Wang, and M.-L. Zhang.
  Partial Label Learning with Discrimination Augmentation.
  In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), pp. 1920-1928, Washington, DC, 2022.
  [paper] [code]
• W. Wang, and M.-L. Zhang.
  Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization.
  In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp. 6982-6993, Online, 2020.
  [paper] [code]

Award

• RIKEN Ohbu Award (Researcher Incentive Award), 2024.
• Scholar/Travel Award, NeurIPS 2023.
• Microsoft Research Asia D-CORE Award, 2023.
• Japanese Government (MEXT) Scholarship, 2023.
• National Scholarship, MOE of PRC, 2021.

Talk

• On the Relation between Complementary-Label Learning and Negative-Unlabeled Learning.
  2023.11.24, International Workshop on Weakly Supervised Learning 2023.

Academic Service

• PC Member/Conference Reviewer: NeurIPS (2023,2024), ICML (2024), ICLR (2024,2025), AISTATS (2025), KDD (2023,2024,2025), SDM (2024), ACML (2024), AAAI (2025).
• Journal Reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Neural Networks, SCIENCE CHINA Information Sciences, ACM Transactions on Intelligent Systems and Technology.
• Web Team Member of KDD 2021.