Takeshi Teshima

Hi! I’m Takeshi Teshima, a 2nd-year Ph.D. student in Machine Learning at the Unversity of Tokyo (Graduate School of Frontier Sciences), advised by Professor Masashi Sugiyama.

I am a member of the Sugiyama-Honda-Yokoya Laboratory, Junior Research Associate at RIKEN AIP (national research center), and I am supported by Masason Foundation.

Previously I received B.Econ. from the University of Tokyo in 2017 and M.Sc. from the Unversity of Tokyo in 2019. In previous years, I have been honored to closely work with Professor Issei Sato as well as many other exceptional collaborators.

Research Interests

I’ve always been interested in generic methodology. I am currently working on developing statistical machine learning as one of the major frontiers of statistics/information technology.

  • Causality for machine learning.
    • Similarity of causal mechanisms as a foundation for transfer learning (causal mechanism transfer).
  • Machine learning for social sciences / natural sciences.
    • Recovering data from ceiling effects, a.k.a. censoring: clipped matrix completion.
  • Machine learning methodology in general.
    • Example: classification learning from limited information (positive-unlabeled classification), robust approximate Bayesian computation, density ratio estimation, and representation power of invertible neural networks.


My CV can be found here.


My email address can be found in the sidebar. If I’m not responsive for more than a few days, it is likely that your mail is wrongly classified as a spam. In such a case, please DM me on Twitter.

News and Upcoming Events

6 Sep 2020

Giving a talk at Math-iine Learning study group.

17 Jul 2020

Presented our work at ICML 2020 and had interesting discussions!

8 Jul 2020

Promoted to a regular member of Masason Foundation! And today is my birthday!

23 Jun 2020

Posted a new preprint: "Coupling-based invertible neural networks are universal diffeomorphism approximators"!

19 Jun 2020

Excited to participate in MLSS 2020 (28 June - 10 July)!

Older news are archived here.