Han Bao

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

  • Sugiyama-Honda-Yokoya Lab at the University of Tokyo [link]
  • 2nd 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. Nordström, M., Bao, H., Löfman, F., Hult, H., Maki, A., & Sugiyama, M.
      Calibrated Surrogate Maximization of Dice.
      In Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2020), LNCS xx:xx-xx, Online, Oct. 4-8, 2020.
      (to appear)
    2. Bao, H., Scott, C., & Sugiyama, M.
      Calibrated Surrogate Losses for Adversarially Robust Classification.
      In Proceedings of 33rd Annual Conference on Learning Theory (COLT2020), PMLR 125:408-451, Online, Jul. 9-12, 2020.
      [link][arXiv][slides]
    3. Bao, H. & Sugiyama, M.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      In Proceedings of 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), PMLR 108:2337-2347, Online, Aug. 26-28, 2020.
      [link][arXiv][slides]
    4. Wu, Y.-H., Charoenphakdee, N., Bao, H., Tangkaratt, V., & Sugiyama, M.
      Imitation Learning from Imperfect Demonstration.
      In Proceedings of 36th International Conference on Machine Learning (ICML2019), PMLR 97:6818-6827, Long Beach, CA, USA, Jun. 9-15, 2019.
      [link][arXiv][poster]
    5. Kuroki, S., Charoenphakdee, N., Bao, H., Honda, J., Sato, I., & Sugiyama, M.
      Unsupervised Domain Adaptation Based on Source-guided Discrepancy.
      In Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI2019), 33 01:4122-4129, Honolulu, HI, USA, Jan. 27-Feb. 1, 2019.
      [link][arXiv]
    6. Bao, H., Niu, G., & Sugiyama, M.
      Classification from Pairwise Similarity and Unlabeled Data.
      In Proceedings of 35th International Conference on Machine Learning (ICML2018), PMLR 80:461-470, Stockholm, Sweden, Jul. 10-15, 2018.
      [link][arXiv][slides][poster]

    Others (non-refereed)

    1. Bao, H. & Sugiyama, M.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      IEICE Technical Report 119:71-78, 2020.
      Presented at 39th Information-Based Induction Sciences and Machine Learning Technical Committee (IBISML039), Kyoto, Japan, Mar. 10-11, 2020.
    2. Bao, H. & Sugiyama, M.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      Presented at 22nd Information-Based Induction Sciences Workshop (IBIS2019), Nagoya, Japan, Nov. 20-23, 2019.
      The winner of the student presentation award.
    3. Shimada, T., Bao, H., Sato, I., & Sugiyama, M.
      Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.
      Presented at 22nd Information-Based Induction Sciences Workshop (IBIS2019), Nagoya, Japan, Nov. 20-23, 2019.
    4. 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.
    5. Bao, H. & Sugiyama, M.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      Presented at Joint Workshop of BBDC, BZML, and RIKEN AIP, Berlin, Germany, Sep. 9-10, 2019.
    6. Shimada, T., Bao, H., Sato, I., & Sugiyama, M.
      Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.
      Presented at 3rd International Workshop on Symbolic-Neural Learning (SNL2019), Tokyo, Japan, Jul. 11-12, 2019.
    7. 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]
    8. Kuroki, S., Charoenphakdee, N., Bao, H., Honda, J., Sato, I., & Sugiyama, M.
      Unsupervised Domain Adaptation Based on Distance between Distributions Using Source-domain Labels.
      Presented at 21st Information-Based Induction Sciences Workshop (IBIS2018), Sapporo, Japan, Nov. 4-7, 2018.
    9. 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 (SNL2017), Nagoya, Japan, Jul. 7-8, 2017.
    10. Bao, H., Sakai, T., Sato, I., & Sugiyama, M.
      Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags.
      IEICE Technical Report 117:55-62, 2017.
      Presented at 29th Information-Based Induction Sciences and Machine Learning Technical Committee (IBISML029), Okinawa, Japan, Jun. 23-25, 2017.
    11. Bao, H., Usui, T., & Matsuura, K.
      Improving Optimization Level Estimation of Malware by Feature Selection.
      Presented at 32nd Symposium on Cryptography and Information Security (SCIS2015), Kokura, Japan, Jan. 20-23, 2015.

    Talks

    Past Talks

    1. 2020/09/10 Talk at RIKEN AIP, Tokyo, Japan.
      Learning Theory Bridges Loss Functions.
      [link][slides]
    2. 2020/09/07 ソシオグローバル情報工学研究センター講演会 — 生産技術研究所，東京．
      損失関数をつなぐ学習理論 (in Japanese)
      [slides]
    3. 2020/07/13 Seminar Talk at Kashima Lab — Kyoto University, Kyoto, Japan.
      Learning Theory Bridges Loss Functions.
      [slides]
    4. 2020/02/07 Seminar Talk at Professor Sanmi Koyejo's Group — University of Illinois at Urbana-Champaign, Champaign, IL, USA.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
      [slides]
    5. 2019/09/23 Modal Seminar — INRIA Lille Nord Europe, Lille, France.
      Unsupervised Domain Adaptation Based on Source-guided Discrepancy.
      [slides]
    6. 2019/09/19 IPAB Seminar — The University of Edinburgh, Edinburgh, UK.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
    7. 2019/09/12 Seminar Talk at Parietal Team — INRIA Paris-Saclay, Paris, France.
      Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
    8. 2019/08/10 CLASP! 第9回，東京．
      スモールデータの機械学習 (in Japanese)．
    9. 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)
    10. 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)．
    11. 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).
    12. 2018/06/18 Science Salon — International Research Center for Neurointelligence, Tokyo, Japan.
      Classification from Pairwise Similarity and Unlabeled Data.
    13. 2017/09/19 Seminar Talk at Sierra Team — INRIA Paris, Paris, France.
      Multiple Instance Learning with Positive 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 (JIML2018) [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

    Languages

    © Han Bao
    Last update: Sep. 10, 2020