High-Dimensional Statistical Modeling Team (RIKEN AIP)

We are focusing on developing machine learning algorithm for science including feature selection, topological data analysis, and transfer learning. In particular, we are interested in nonlinear algorithms for high-dimensional data. Ultimately, we want to build a machine learning framework to automatically find an important information from data.

Currently, we are focusing on

  1. Selective inference with kernels.

  2. High-dimensional nonlinear feature selection.

  3. Optimal transport.


Ph.D. student
We accept graduate students from Graduate School of Informatics, Kyoto University.

E-mail: \(\texttt{makoto.yamada@riken.jp}\)


2020/06/10 Makoto is serving area chair of ICLR 2021.
2020/05/01 Hector joined our laboratory! Welcome!
2020/04/01 New members joind our laboratory! Welcome!
2020/03/10 Makoto is serving area chairs of ICML2020, NeurIPS 2020, and IJCAI 2020.
2020/02/25 One paper has been accepted to CVPR 2020.
2020/01/14 Topological Bayesian Optimization paper has been accepted to ECAI 2020.
2020/01/07 Two papers have been accepted to AISTATS 2020.
2019/11/11 One paper has been accepted to AAAI 2020.
2019/09/04 Three papers have been accepted to NeurIPS 2019.
2019/08/12 Our kernel based attention paper has been accepted to EMNLP-IJCNLP 2019.
2019/04/22 Tam's safe screening paper has been accepted to ICML 2019.
2019/04/10 Our nonlinear feature selection paper has been accepted to ISMB/ECCB 2019.


RIKEN Kyoto Office
Research Bldg. No.15
Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto