Publications and Preprints 🔗

  1. 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, accepted (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%).

    Pre-proceedings code (figure) slides Conference session
  2. Fujisawa, M., Teshima, T., and Sato, I.,
    γ-ABC: Outlier-robust approximate bayesian computation based on robust divergence estimator. arXiv:2006.07571 [stat.ML].
    preprint (arXiv)
  3. Kato, M. and Teshima, T.,
    Non-negative Bregman divergence minimization for deep direct density ratio estimation. arXiv:2006.06979 [cs.LG].
    preprint (arXiv)
  4. 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).
    paper
  5. 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