Han Bao

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

  • Sugiyama-Sato-Honda Lab at the University of Tokyo [link]
  • 2nd year master 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, loss function theory, and optimization.

    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. 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]

    Others (workshops / domestic conferences)

    1. 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)
    2. 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.

    Invited Talks

    Upcoming Talks

    Past Talks

    1. 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).
    2. 2018/06/18 Science Salon — International Research Center for Neurointelligence, Tokyo, Japan.
      Classification from Pairwise Similarity and Unlabeled Data.

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