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RIKEN Center for Advanced Intelligence Project High-Dimensional Causal Analysis Team

Team Leader: Masaaki Imaizumi (Ph.D.)

Research Summary

Masaaki Imaizumi

The High-Dimensional Causal Analysis team studies structures in high-dimensional data, such as the causality. In today's world of advanced data acquisition, storage, and analysis technologies, not only the structures possessed by data, but also the data science techniques for analyzing them have become highly complex. This team aims to understand both the large-degree-of-freedom, high-dimensional, and complex structures of data and the techniques for analyzing them, and to construct and extend theories based on these understandings. As specific methodologies, we study modern high-dimensional statistics and deep learning theory, and use them to develop methods of analysis and statistical inference for causal structures.

Main Research Fields

  • Informatics

Related Research Fields

  • Mathematical & Physical Sciences
  • Principles of Informatics/Statistical science
  • Principles of Informatics/Mathematical informatics

Keywords

  • High-dimensional statistics
  • Deep learning theory
  • Statistical learning theory
  • Statistical causal inference

Selected Publications

  • 1. T.Tsuda, M.Imaizumi.:
    "Benign Overfitting of Non-Sparse High-Dimensional Linear Regression with Correlated Noise"
    Electronic Journal of Statistics, 18(2), (2024).
  • 2. S.Nakakita, P.Alquier, M.Imaizumi.:
    "Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution"
    Electronic Journal of Statistics, 18(1), (2024).
  • 3. J.Komiyama, M.Imaizumi.:
    "High-dimensional Contextual Bandit Problem without Sparsity"
    Advances in Neural Information Processing Systems, 36, (2023).
  • 4. R.Zhang, M.Imaizumi, B.Schölkopf, K.Muandet.:
    "Instrumental Variable Regression via Kernel Maximum Moment Loss"
    Journal of Causal Inference, 11(1), (2023).
  • 5. M.Imaizumi, J.Schmidt-Hieber.:
    "On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization"
    IEEE Transaction on Information Theory, 69(2), (2023).
  • 6. M.Imaizumi, K.Fukumizu.:
    "Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurface"
    Journal of Machine Learning Research, 23(111), (2022),
  • 7. R.Nakada, M.Imaizumi.:
    "Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality"
    Journal of Machine Learning Research 21(174), (2020).
  • 8. M.Imaizumi, K.Fukumizu.:
    "Deep Neural Networks Learn Non-Smooth Functions Effectively"
    PMLR: Artificial Intelligence and Statistics, (2019).
  • 9. M.Imaizumi, T.Maehara, Y.Yoshida.:
    "Statistically Efficient Estimation for Non-Smooth Probability Densities"
    PMLR: Artificial Intelligence and Statistics, (2018).
  • 10. M.Imaizumi, T.Maehara, K.Hayashi.:
    "On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm"
    Advances in Neural Information and Processing Systems 30, (2018).

Lab Members

Principal investigator

Masaaki Imaizumi
Team Leader

Core members

Guillaume Braun
Postdoctoral Researcher
Hanna Tseran
Postdoctoral Researcher
Shogo Nakakita
Visiting Scientist
Kota Matsui
Visiting Scientist
Hirofumi Ohta
Visiting Scientist
Jacopo Peroni
Student Trainee
Eshant English
Student Trainee
Mana Sakai
Research Part-time Worker II

Contact Information

Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi, Chuo-ku, Tokyo
103-0027, Japan
Email: masaaki.imaizumi@riken.jp

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