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-Honda-Yokoya 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 🔗

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!

02 Mar 2021

Posted a new preprint: "Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation"!

Older news are archived here.