張 一凡 チョウ イーファン
Hi, this is Yivan!
I’m a Ph.D. student supervised by Prof. Masashi Sugiyama at the University of Tokyo.
My research interest lies in the theory and application of machine learning, especially those abstract and fundamental problems that can be instantiated in seemingly different fields.
My current focus is the case where the data we have and the task we want to solve do not match, including:
- Weakly supervised learning
- Representation learning
- Domain adaptation/generalization
I don’t want to invent something. I want to discover something.
My research is generously supported by RIKEN Center for Advanced Intelligence Project and Microsoft Research Asia.
Approximating Instance-Dependent Noise via Instance-Confidence Embedding
Yivan Zhang and Masashi Sugiyama
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Yivan Zhang, Gang Niu, and Masashi Sugiyama
Learning from Aggregate Observations
Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, and Masashi Sugiyama
Learning from Indirect Observations
Yivan Zhang, Nontawat Charoenphakdee, and Masashi Sugiyama