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.
We presented our paper "Imitation Learning from Imperfect Demonstration" in ICML2019!
This work provides a methodology for imitation learning under the situations where not all demonstrations are reliable and their reliability scores can be obtained.
You can find our paper and poster.
I started Ph.D. course from this April in UTokyo. When you visit Tokyo, please feel free to contact me.
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 AAAI, 2019 (to appear). [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][slide][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.
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 (Statistical Classification Based on Weakly-supervised Data)．
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).