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 and optimization.
During the stay in the US, I'll be back to Japan once in the middle of November (around 15-25) to attend ACML, IBIS, and so on. If you want to talk with me, we can meet at Nagoya.
I'm going to visit Prof. Clayton Scott at University of Michigan (Ann Arbor) from October 15th through Feburuary 12th, 2020, and work on a research project related to learning theory. If you get close to Ann Arbor, please do not hesitate to contact me! Ann Arbor is also close to Detroit and Toronto.
I started Ph.D. course from this April in UTokyo. When you visit Tokyo, please feel free to contact me. (now in Ann Arbor until Feb. 2020)
Bao, H. & Sugiyama, M. Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. [arXiv]
Shimada, T., Bao, H., Sato, I., & Sugiyama, M. Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. [arXiv]
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)
Wu, Y.-H., Charoenphakdee, N., Bao, H., Tangkaratt, V., & Sugiyama, M. Imitation Learning from Imperfect Demonstration. In Proceedings of International Conference on Machine Learning (ICML2019), PMLR 97:6818-6827, 2019. [link][arXiv][poster]
Kuroki, S., Charoenphakdee, N., Bao, H., Honda, J., Sato, I., & Sugiyama, M. Unsupervised Domain Adaptation Based on Source-guided Discrepancy. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI2019), 33 01:4122-4129, 2019. [link][arXiv]
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][slides][poster]
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.
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.
2019/09/23 Modal Seminar, Lille, France. Unsupervised Domain Adaptation Based on Source-guided Discrepancy. [slides]
2019/08/10 CLASP! 第9回，東京． スモールデータの機械学習 (in Japanese)．