Publications and Preprints 🔗

  1. Oishi, A., Teshima, T., Kojima, N., Kiriha, M., Kojima, N., Sasaki, T., Takahira, K., Takeuchi, T., Tajima, K., Noda, C., Hirose, H., and Yamanaka, S.,
    Forecasting the movement of internally displaced people by artificial intelligence (Book Chapter). Digital Innovations, Business and Society in Africa: New Frontiers and a Shared Strategic Vision (Springer), accepted.
    Book series: Advances in Theory and Practice of Emerging Markets". Book editors: Richard Boateng, Sheena Lovia Boateng, Thomas Anning-Dorson and Longe Olumide Babatope"

  2. Teshima, T., and Sugiyama, M.,
    Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. 37th Conference on Uncertainty in Artificial Intelligence, accepted (UAI 2021).
    preprint (arXiv)
  3. Teshima, T., Tojo, K., Ikeda, M., Ishikawa, I., and Oono, K.,
    Universal approximation property of neural ordinary differential equations. NeurIPS2020 Workshop: Differential Geometry meets Deep Learning, 2020 (DiffGeo4DL).
    preprint (arXiv)
  4. Teshima, T.*, Ishikawa, I.*, Tojo, K., Oono, K., Ikeda, M., and Sugiyama, M.,
    Coupling-based invertible neural networks are universal diffeomorphism approximators. Thirty-fourth Conference on Neural Information Processing Systems, 2020 (NeurIPS 2020).
    * Equal contribution. Oral presentation (one of the 105 orals among the 1900 accepted papers; paper acceptance rate 20.1%, oral acceptance rate 1.1%).

    Proceedings code (figure) slides Conference session
  5. Fujisawa, M., Teshima, T., and Sato, I.,
    γ-ABC: Outlier-robust approximate bayesian computation based on robust divergence estimator. The 24th International Conference on Artificial Intelligence and Statistics, accepted (AISTATS 2021).
    preprint (arXiv) conference
  6. Kato, M. and Teshima, T.,
    Non-negative Bregman divergence minimization for deep direct density ratio estimation. Thirty-eighth International Conference on Machine Learning, accepted (ICML 2021).
    preprint (arXiv)
  7. Kato, M., Teshima, T., and Honda, J.,
    Learning from positive and unlabeled data with a selection bias. Seventh International Conference on Learning Representations, 2019 (ICLR 2019).
  8. Teshima, T., Xu, M., Sato, I., and Sugiyama, M.,
    Clipped matrix completion: a remedy for ceiling effects. Thirty-Third AAAI Conference on Artificial Intelligence, 2019 (AAAI-19).
    paper supplementary code slides

Selected Poster Presentations (Physical/Virtual) More...

  1. Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
    December 07, 2020  Online   NeurIPS 2020 poster
  2. Few-shot Domain Adaptation by Causal Mechanism Transfer
    July 17, 2020  Online   International Conference on Machine Learning (ICML) 2020 slides

Selected Talks More...

Work Experiences 🔗

Education 🔗

Awards and Grants 🔗

  • RIKEN Ohbu Award 2020 (Research Incentive Award)
  • (17 Mar 2021)
  • IBIS 2020 (The 23rd Information-Based Induction Sciences Workshop) Outstanding Presentation Award
  • (Jan 2021)
  • Masason Foundation regular member
  • (Jul 2020 - present)
  • UTokyo Toyota-Dwango AI scholarship (merit-based)
  • (Apr 2020 - Mar 2021)
  • Academic Research Grant for GSFS Doctor Course Students
  • (1 Jun 2019 - 30 Nov 2019)
  • Masason Foundation associate member
  • (1 Jul 2019 - Jun 2020)
  • UTokyo Toyota-Dwango AI scholarship (merit-based)
  • (Apr 2019 - Mar 2020)
  • Full exemption of repayment of Type I scholarship for excellent performance, Japan Student Services Organization (JASSO)
  • (2019)
  • Deans' Award for Outstanding Achievement (Master Course; Graduate School of Frontier Sciences)
  • (20 Mar 2019)
  • UTokyo Toyota-Dwango AI scholarship (merit-based)
  • (Sep 2017 - Mar 2018)

    Selected Certificates and Licenses 🔗

    Other Professional Activities 🔗

    Downloadable CV (PDF): Summary version, Full version 🔗