張 一凡 · チョウ イーファン · [ʈʂāŋ īː fǽn]

[yivan.xyz@gmail.com] [CV] [Google Scholar] [DBLP] [GitHub]

Hi, this is Yivan! I’m currently doing research at RIKEN AIP with Masashi Sugiyama. Previously, I received my PhD from the University of Tokyo.

I work on the theory and application of machine learning, especially those fundamental and foundational problems that can be instantiated in seemingly different fields. I’m currently working on applied category theory in machine learning. I love things that were discovered.

My research is/was generously supported by RIKEN Center for Advanced Intelligence Project (RIKEN AIP), Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), and Microsoft Research Asia (MSRA).

Publications

Conference

• Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
  Yivan Zhang, Masashi Sugiyama
  Neural Information Processing Systems 2024 (NeurIPS’24)
  Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at International Conference on Machine Learning 2023 (ICML’23)
  [arXiv] [OpenReview] [NeurIPS’24] [TAG-ML’23] [poster]
• A Category-theoretical Meta-analysis of Definitions of Disentanglement
  Yivan Zhang, Masashi Sugiyama
  International Conference on Machine Learning 2023 (ICML’23)
  International Workshop on Symbolic-Neural Learning 2023 (SNL’23)
  [arXiv] [OpenReview] [ICML’23] [slides] [poster]
• Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
  Yivan Zhang, Gang Niu, Masashi Sugiyama
  International Conference on Machine Learning 2021 (ICML’21)
  [arXiv] [ICML’21] [code] [slides] [poster]
• Learning from Aggregate Observations
  Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama
  Neural Information Processing Systems 2020 (NeurIPS’20)
  [arXiv] [NeurIPS’20] [code]