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, 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).
  • 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 🔗

04 Mar 2020

I will give an invited talk at the organized session 『異なるタスクを活用する機械学習:転移学習,メタ学習』 at IBISML from 14:40 until 15:20.

03 Mar 2020

I will give an invited talk at Workshop on Functional Inference and Machine Intelligence (FIMI) from 11:20 until 12:20 (with Prof. Ishikawa from Ehime University).

23 Jan 2021

Our paper "γ-ABC: Outlier-Robust Approximate Bayesian Computation based on A Robust Divergence Estimator" has been accepted at AISTATS 2021!

08 Jan 2021

We have been awarded IBIS 2020 Outstanding Presentation Award!

17 Dec 2020

Our work has been mentioned in Nikkei XTech online news articles here (Dec 17) and here (Dec 25).

Older news are archived here.