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RIKEN Center for Advanced Intelligence Project Structured Learning Team

Team Leader: Yoshinobu Kawahara (D.Eng.)

Research Summary

Yoshinobu  Kawahara(D.Eng.)

When making predictions based on intelligent information processing such as machine learning, we usually have prior information about structures among variables in data. In our team, we study theories and algorithms for learning with such structural information. Moreover, we conduct applied researches by applying developed algorithms to a variety of scientific and engineering data.

Research Subjects:

  • Machine learning with structural prior information
  • Development of optimization algorithms for efficient learning
  • Learning for analysis of spatiotemporal dynamics
  • Application of developed methods to scientific and engineering data

Main Research Fields

  • Informatics

Related Research Fields

  • Engineering
  • Mathematical & Physical Sciences
  • Mathematical informatics
  • Statistical science
  • Intelligent informatics


  • Machine Learning
  • Data Science
  • Learning with Prior Information
  • Dynamics
  • Optimization

Selected Publications

  • 1. Weissenbacher, M., Sinha, S., Garg, A., and Kawahara, Y.:
    "Koopman Q-learning: Offline reinforcement learning via symmetries of dynamics"
    Proceedings of the 39th International Conference on Machine Learning (ICML'22) (accepted)
  • 2. Hashimoto, Y., Ishikawa, I., Ikeda, M., Komura, F., Katsura, T., and Kawahara, Y.:
    "Reproducing kernel Hilbert C*-modules and kernel mean embeddings"
    Journal of Machine Learning Research, Vol.22, No.267, pp.1-56 (2021)
  • 3. Fujii, K., Takeishi, N., Tsutui, K., Fujioka, E., Nishiumi, N., Tanaka, R., Fukushiro, M., Ide, K., Kohno, H., Yoda, K., Takahashi, S., Hiryu, S., and Kawahara, Y.:
    "Learning interactions rules from multi-animal trajectories via augmented behavioral models"
    Advances in Neural Information Processing Systems 34, pp.11108-11122 (2021)
  • 4. Takeishi, N., and Kawahara, Y.:
    "Learning dynamics models with stable invariant sets"
    Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp.9782-9790 (2021)
  • 5. Hashimoto, Y., Ishikawa, I., Ikeda, M., Matsuo, Y. and Kawahara, Y.:
    "Krylov subspace method for nonlinear dynamical systems with random noise"
    Journal of Machine Learning Research, 21(172): 1-29 (2020)
  • 6. Takeishi, N. and Kawahara, Y.:
    "Knowledge-based regularization in generative modeling"
    Proceedings of the 29th International joint Conference on Artificial Intelligence (IJCAI’20), pp.2390-2396 (2020)
  • 7. Takeuchi, N., Yoshida, Y., and Kawahara, Y.:
    "Variational inference of penalized regression with submodular functions"
    Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI'19), 443 (2019)
  • 8. Fujii, K. and Kawahara, Y.,:
    "Dynamic mode decomposition with vector-valued reproducing kernels for extracting dynamical structures among observables"
    Neural Networks, 117: 94-103 (2019)
  • 9. Ishikawa, I., Fujii, K., Ikeda, M., Hashimoto, Y., and Kawahara, Y.:
    "Metric for nonlinear dynamical systems with Koopman operators"
    Advances in Neural Information Processing Systems 31, pp.2858-2868 (2018)
  • 10. Takeishi, N., Kawahara, Y., and Yairi, T.:
    "Learning Koopman invariant subspaces for dynamic mode decomposition"
    Advances in Neural Information Processing Systems 30, pp.1130-1140 (2017)

Related Links

Lab Members

Principal investigator

Yoshinobu Kawahara
Team Leader

Core members

Matthias Weissenbacher
Research Scientist
Daisuke Hatano
Postdoctoral Researcher
Velmurugan Gandhi
Postdoctoral Researcher
Itsushi Sakata
Postdoctoral Researcher
Yoshiteru Nishimura
Technical Staff I
Keisuke Fujii
Visiting Scientist
Shunji Umetani
Visiting Scientist
Naoya Takeishi
Visiting Scientist
Takuya Konishi
Visiting Scientist
Yuka Hashimoto
Visiting Scientist

Contact Information

744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka 819-0395 Japan
Email: yoshinobu.kawahara [at] riken.jp