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 learning and inference from label-scarce, imbalanced, and unreliable data. I am also interested in statistical learning theory and optimization.

    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.
    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. 2018/11/20 Best Poster Award at 1st Japan-Israel Machine Learning Meeting [link]
    2. 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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