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.

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 (2024,2025), 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, Journal of Artificial Intelligence Research, International Journal of Computer Vision, Neural Networks, SCIENCE CHINA Information Sciences, ACM Transactions on Intelligent Systems and Technology.
• Web Team Member of KDD 2021.