Publication

Books

  1. Sugiyama, M., Bao, H., Ishida, T., Lu, N., Sakai, T., & Niu, G.
    Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach, MIT Press, Cambridge, MA, USA, 2022.
    [link]

Journal Articles (refereed)

  1. Bao, H. & Sakaue, S.
    Sparse Regularized Optimal Transport with Deformed q-Entropy.
    Entropy, 24(11):1634, 2022.
    [link]
  2. Yamada, M., Takezawa, Y., Sato, R., Bao, H., Kozareva, Z., & Ravi, S.
    Approximating 1-Wasserstein Distance with Trees.
    Transactions on Machine Learning Research, 2022.
    [link][arXiv]
  3. 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]
  4. 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.
    Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds.
    In Proceedings of 36th Annual Conference on Learning Theory (COLT2023) PMLR XXX:XXX-XXX, Bangalore, India, Jul. 12-15, 2023.
    [link (to appear)]
  2. Arase, Y., Bao, H., & Yokoi, S.
    Unbalanced Optimal Transport for Unbalanced Word Alignment.
    In Proceedings of 61st Annual Meeting of the Association for Computational Linguistics (ACL2023) XXX-XXX, Toronto, Canada, Jul. 9-14, 2023.
    [link (to appear)]
  3. Nakamura, S., Bao, H., & Sugiyama, M.
    Robust Computation of Optimal Transport by β-potential Regularization.
    In Proceddings of 14th Asian Conference on Machine Learning (ACML2022) PMLR 189:770-785, Hyderabad, India, Dec. 12-14, 2022.
    [link][arXiv]
  4. Bao, H., Nagano, Y., & Nozawa, N.
    On the Surrogate Gap between Contrastive and Supervised Losses.
    In Proceedings of 39th International Conference on Machine Learning (ICML2022), PMLR 162:1585-1606, Baltimore, MD, USA, Jul. 17-23, 2022.
    [link][arXiv][poster][github] (equal contribution & alphabetical ordering)
  5. Bao, H.*, Shimada, T.*, Xu, L., Sato, I., & Sugiyama, M.
    Pairwise Supervision Can Provably Elicit a Decision Boundary.
    In Proceedings of 25th International Conference on Artificial Intelligence and Statistics (AISTATS2022), PMLR 151:2618-2640, online, Mar. 28-30, 2022.
    [link][arXiv][poster] (* equal contribution)
  6. Dan, S., Bao, H., & Sugiyama, M.
    Learning from Noisy Similar and Dissimilar Data.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD2021), LNCS 12976:233-249, online, Sep. 13-17, 2021.
    [link][arXiv]
  7. 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]
  8. 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]
  9. 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 (corrigendum)][slides] (arXiv version contains a corrigendum; the definition of calibrated losses is modified)
  10. 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]
  11. 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]
  12. 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]
  13. 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]

Preprints

  • Takezawa, Y., Sato, R., Bao, H., Niwa, K., & Yamada, M.
    Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.
    [arXiv]
  • Hataya, R., Bao, H., & Arai, H.
    Will Large-scale Generative Models Corrupt Future Datasets?
    [arXiv]
  • Takezawa, Y., Bao, H., Niwa, K., Sato, R., & Yamada, M.
    Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data.
    [arXiv]