Publication

Preprints

  • Bao, H.*, Shimada, T.*, Xu, L., Sato, I., & Sugiyama, M.
    Similarity-based Classification: Connecting Similarity Learning to Binary Classification.
    [arXiv] (* equal contribution)
  • Dan, S., Bao, H., & Sugiyama, M.
    Learning from Noisy Similar and Dissimilar Data.
    [arXiv]

Journal Articles

  1. Shimada, T., Bao, H., Sato, I., & Sugiyama, M.
    Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.
    Neural Computation, 33(5):1234-1268, 2021.
    [link][arXiv]
  2. 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. & Sugiyama, M.
    Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation.
    In Proceedings of 24th International Conference on Artificial Intelligence and Statistics (AISTATS2021), PMLR 130:1648-1656, Online, Apr. 13-15, 2021.
    [link][poster][github]
  2. Nordström, M., Bao, H., Löfman, F., Hult, H., Maki, A., & Sugiyama, M.
    Calibrated Surrogate Maximization of Dice.
    In Proceedings of 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2020), LNCS 12264:269-278, Online, Oct. 4-8, 2020.
    [link]
  3. 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]
  4. 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]
  5. 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][github]
  6. 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]
  7. 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][github]