Takeshi Teshima

Hi! I’m Takeshi Teshima, a third-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-Yokoya-Ishida Laboratory, a 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.

I am taking my first step toward AI for Good.

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. For more details, please visit the research introduction page.

  • Causality for machine learning.
    • Similarity of causal mechanisms as a foundation for transfer learning (causal mechanism transfer).
    • Incorporating causal graphical knowledge into predictive modeling in general (causal data augmentation).
  • 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.

CV 🔗

My CV can be found here.

Contacts 🔗

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 🔗

1 Jul 2021

Our work on invertible neural networks has been featured in the PR magazine RIKEN!

13 May 2021

Our paper "Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation" has been accepted at UAI 2021!

8 May 2021

Our paper "Non-negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation" has been accepted at ICML 2021!

22 Mar 2021

Thrilled to join Peloria Insights until the end of May as a data scientist (individual contractor)!

17 Mar 2021

I have been awarded the Ohbu Award 2020 (Research Incentive Award) from RIKEN!

Older news are archived here.