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RIKEN Center for Brain Science Statistical Mathematics Collaboration Unit

Unit Leader: Takeru Matsuda (Ph.D.)

Research Summary

Takeru Matsuda

With recent advances in experimental technologies, large-scale and diverse brain data are now available. To extract more information from such brain data, we develop tailored statistical methods by translating the characteristics of data into the form of statistical models. We incorporate techniques from applied mathematics, such as numerical analysis and optimization, to develop efficient methods that are applicable to large-scale data. We also investigate the fundamental theory of statistics.

Main Research Fields

  • Informatics

Related Research Fields

  • Interdisciplinary Science & Engineering
  • Mathematical & Physical Sciences
  • Statistical science
  • Mathematical informatics

Keywords

  • Statistics
  • Applied mathematics
  • Data analysis
  • Machine learning

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

  • 1.Matsuda, T.
    "Adapting to general quadratic loss via singular value shrinkage"
    IEEE Transactions on Information Theory 70, 3640--3657, 2024.
  • 2.Amari, S. and Matsuda, T.
    "Information geometry of Wasserstein statistics on shapes and affine deformations"
    Information geometry, accepted.
  • 3.Matsuda, T.
    "Inadmissibility of the corrected Akaike information criterion"
    Bernoulli 30, 1416--1440, 2024.
  • 4.Matsuda, T. and Strawderman, W. E.
    "Estimation under matrix quadratic loss and matrix superharmonicity"
    Biometrika 109, 503-519, 2022.
  • 5.Matsuda, T. and Soma. T.
    "Information geometry of operator scaling"
    Linear Algebra and Its Applications 649, 240-267, 2022.
  • 6.Matsuda, T., Homae. F., Watanabe, H., Taga, G. and Komaki, F.
    "Oscillator decomposition of infant fNIRS data"
    PLOS Computational Biology 18(3), e1009985, 2022.
  • 7.Matsuda, T., Uehara, M. and Hyvarinen, A.
    "Information criteria for non-normalized models"
    Journal of Machine Learning Research 22(158):1-33, 2021.
  • 8.Matsuda. T. and Miyatake, Y.
    "Estimation of ordinary differential equation models with discretization error quantification"
    SIAM/ASA Journal on Uncertainty Quantification 9, 302-331, 2021.
  • 9.松田 孟留
    "競技かるたの決まり字に関する統計的解析"
    応用統計学 49, 1--11, 2020.
  • 10.Matsuda, T. and Hyvarinen, A.
    "Estimation of non-normalized mixture models"
    22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).

Related Links

Lab Members

Principal investigator

Takeru Matsuda
Unit Leader

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