Talks

Upcoming talks

    Comming soon!

Past workshop talks (non-refereed)

  1. Bao, H., Scott, C., & Sugiyama, M.
    Calibrated Surrogate Losses for Adversarially Robust Classification.
    Presented at 23rd Information-Based Induction Sciences Workshop (IBIS2020), Online, Nov. 23-26, 2020.
    The winner of the best presentation award.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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]
  9. 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.
  10. 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.
  11. 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.
  12. 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.

Past invited talks

  1. 2021/03/26 Michinoku Communication Science Seminar at Tohoku NLP Lab — Tohoku University
    Learning from Excess Risk Transfer Perspective.
  2. 2020/12/22 東芝研究開発センター シンポジウム.
    敵対的学習における適合的な損失関数.
  3. 2020/12/15 Talk event: Learning theory of loss functions.
    Calibrated Surrogate Losses and Robust Learning.
    [link][slides]
  4. 2020/09/10 Talk at RIKEN AIP, Tokyo, Japan.
    Learning Theory Bridges Loss Functions.
    [link][slides]
  5. 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]
  6. 2020/07/13 Seminar Talk at Kashima Lab — Kyoto University, Kyoto, Japan.
    Learning Theory Bridges Loss Functions.
    [slides]
  7. 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]
  8. 2019/09/23 Modal Seminar — INRIA Lille Nord Europe, Lille, France.
    Unsupervised Domain Adaptation Based on Source-guided Discrepancy.
    [slides]
  9. 2019/09/19 IPAB Seminar — The University of Edinburgh, Edinburgh, UK.
    Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
  10. 2019/09/12 Seminar Talk at Parietal Team — INRIA Paris-Saclay, Paris, France.
    Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
  11. 2019/08/10 CLASP! 第9回,東京.
    スモールデータの機械学習 (in Japanese)
  12. 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)
  13. 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).
  14. 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).
  15. 2018/06/18 Science Salon — International Research Center for Neurointelligence, Tokyo, Japan.
    Classification from Pairwise Similarity and Unlabeled Data.
  16. 2017/09/19 Seminar Talk at Sierra Team — INRIA Paris, Paris, France.
    Multiple Instance Learning with Positive and Unlabeled Data.