Han Bao

Han Bao (cn) / Fukumu Tsutsumi (ja) / 包 含

  • Sugiyama-Sato-Honda Lab at the University of Tokyo [link]
  • 1st year Ph.D. student in Dept. of Computer Science [link]
  • Supervisor: Masashi Sugiyama
  • tsutsumi[at]ms.k.u-tokyo.ac.jp
  • Resume

    • About me: I am a graduate student doing research on machine learning. My research interest lies in statistical learning theory especially regarding loss functions. In addition, transfer learning and similarity learning are in my favor.

    News

    Publications

    Preprints

    Journal Articles

    1. Bao, H., Sakai, T., Sato, I., & Sugiyama, M.
      Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags.
      Neural Networks 105:132-141, 2018.
      [link][arXiv]

    Conference Proceedings (refereed)

    1. Wu, Y.-H., Charoenphakdee, N., Bao, H., Tangkaratt, V., & Sugiyama, M.
      Imitation Learning from Imperfect Demonstration.
      In Proceedings of International Conference on Machine Learning (ICML2019), PMLR 97:6818-6827, 2019.
      [link][arXiv][poster]
    2. Kuroki, S., Charoenphakdee, N., Bao, H., Honda, J., Sato, I., & Sugiyama, M.
      Unsupervised Domain Adaptation Based on Source-guided Discrepancy.
      In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI2019), 33 01:4122-4129, 2019.
      [link][arXiv]
    3. Bao, H., Niu, G., & Sugiyama, M.
      Classification from Pairwise Similarity and Unlabeled Data.
      In Proceedings of International Conference on Machine Learning (ICML2018), PMLR 80:461-470, 2018.
      Presented at ICML2018, Stockholm, Sweden, 10-15, Jul., 2018.
      [link][arXiv][slides][poster]

    Others (non-refereed)

    1. Bao, H. & Sugiyama, M.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      Presented at IBIS2019, Nagoya, Japan, Nov. 20-23, 2019.
      The winner of the student presentation award.
    2. Bao, H. & Sugiyama, M.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      Presented at UK-Japan robotics and AI research collaboration workshops, Edinburgh, UK, Sep. 17-18, 2019.
    3. Bao, H., Niu, G., & Sugiyama, M.
      Classification from Pairwise Similarity and Unlabeled Data.
      Presented at 1st Japan-Israel Machine Learning Meeting (JIML-2018), Tel-Aviv, Israel, Nov. 19-20, 2018.
      The winner of the best poster award.
      [poster]
    4. Bao, H., Sakai, T., Sugiyama, M., & Sato, I.
      Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags.
      Presented at 1st International Workshop on Symbolic-Neural Learning (SNL-2017), Nagoya, Japan, July 7-8, 2017.
    5. Bao, H., Usui, T., & Matsuura, K.
      Improving Optimization Level Estimation of Malware by Feature Selection (in Japanese).
      In Proceedings of the 32nd Symposium on Cryptography and Information Security, 2015.

    Talks

    Upcoming Talks

    Past Talks

    1. 2019/09/23 Modal Seminar — INRIA Lille Nord Europe, France.
      Unsupervised Domain Adaptation Based on Source-guided Discrepancy.
      [slides]
    2. 2019/09/19 IPAB Seminar — The University of Edinburgh, UK.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
    3. 2019/08/10 CLASP! 第9回，東京．
      スモールデータの機械学習 (in Japanese)．
    4. 2019/08/08 東京大学理学部オープンキャンパス2019，東京．
      人工知能は人間の夢を見るか？ (in Japanese)
    5. 2018/10/29 第8回脳型人工知能とその応用に関するミニワークショップ — ATR，京都．
      弱教師付きデータを用いた統計的分類 (in Japanese)．
    6. 2018/08/12 第3回 統計・機械学習若手シンポジウム，東京．
      Classification from Pairwise Similarity and Unlabeled Data (in Japanese).
    7. 2018/06/18 Science Salon — International Research Center for Neurointelligence, Tokyo, Japan.
      Classification from Pairwise Similarity and Unlabeled Data.

    Grants

    Awards

    1. 2019/11/22 Student Presentation Award at IBIS2019 [link]
    2. 2018/11/20 Best Poster Award at 1st Japan-Israel Machine Learning Meeting [link]
    3. 2018/04/27 AIP Network Lab Award (1st place / 40 researchers) [link1][link2] / AIP Challenge Program

    Professional Activities

    Reviewer

    Education

    Employment

    Other Activities

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