publications

My Google Scholar profile is here.


Book Chapters

  1. Nan Lu*, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, and Masashi Sugiyama
    In: Federated and Transfer Learning, 2022
  2. Ayaka Oishi*, Takeshi Teshima, Kunikazu Akao, Tsuyoshi Kano, Megumi Kiriha, Naoki Kojima, Takuya Sasaki, Kentaro Takahira, Jumpei Takami, Tomonari Takeuchi, Kenji Tajima, Chihiro Noda, Hikaru Hirose, and Shigeki Yamanaka
    In: Digital Innovations, Business and Society in Africa: New Frontiers and a Shared Strategic Vision, 2022

Journal Articles (Refereed)

  1. Isao Ishikawa*, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, and Masashi Sugiyama
    Journal of Machine Learning Research, 2023
    (First two authors contributed equally.)

Conference Proceedings (Refereed)

  1. Takeshi Teshima*, and Masashi Sugiyama
    In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2021), PMLR 161, 2021
    (Acceptance rate: 26.4%)
  2. Masahiro Kato*, and Takeshi Teshima
    In: Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 2021
    (Acceptance rate: 21.4%)
  3. Masahiro Fujisawa*, Takeshi Teshima, Issei Sato, and Masashi Sugiyama
    In: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), PMLR 130, 2021
    (Acceptance rate: 29.8%)
  4. Takeshi Teshima*, Issei Sato, and Masashi Sugiyama
    In: Proceedings of the 37th International Conference on Machine Learning (ICML2020), PMLR 119, 2020
    (Acceptance rate: 21.8%)
  5. Takeshi Teshima*, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, and Masashi Sugiyama
    In: Advances in Neural Information Processing Systems 33, 2020
    (First two authors contributed equally. Selected for oral presentation; 105 orals out of 1900 accepted papers. Acceptance rate 20.1%, oral acceptance rate 1.1%)
  6. Takeshi Teshima*, Miao Xu, Issei Sato, and Masashi Sugiyama
    In: Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2019
    (Acceptance rate: 16.2%)
  7. Learning from positive and unlabeled data with a selection bias
    Masahiro Kato*, Takeshi Teshima, and Junya Honda
    In: Proceedings of the Seventh International Conference on Learning Representations (ICLR 2019), 2018
    (No page numbers were assigned in these proceedings.)

Workshop Papers

  1. Takeshi Teshima*, Koichi Tojo, Masahiro Ikeda, Isao Ishikawa, and Kenta Oono
    2020
    (Presented at NeurIPS2020 Workshop: Differential Geometry meets Deep Learning, DiffGeo4DL.)

Patents and Patent Applications

  1. Takeshi Teshima, and Hiroshi Kajino
    2020
    (US Patent App. 17/060,862; Publication No. US20220108765A1. Patent pending.)

Theses

  1. Causal Machine Learning from Small Data: A Data Augmentation Approach
    Takeshi Teshima
    The University of Tokyo, 2022
    (UTokyo repository forthcoming)
  2. Takeshi Teshima
    The University of Tokyo, 2019