Dr. Bo Han

Dr. Han is currently an Assistant Professor of Computer Science at Hong Kong Baptist University, and a Visiting Scientist at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), hosted by Masashi Sugiyama. He was a Postdoc Fellow at RIKEN AIP (2019-2020), advised by Masashi Sugiyama. He received his Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019), advised by Ivor W. Tsang and Ling Chen. During 2018-2019, he was a Research Intern with the AI Residency Program at RIKEN AIP, working on robust deep learning projects with Masashi Sugiyama, Gang Niu and Mingyuan Zhou. He has served as area chairs of NeurIPS'20 and ICLR'21, and program committes of ICML, AISTATS, UAI, AAAI, IJCAI and ACML. He received the RIKEN BAIHO Award (2019), RGC Early Career Scheme (2020) and NSFC Young Scientists Fund (2020).

Research Interests

• Weakly-supervised Machine Learning [NeurIPS'18]
• Security, Privacy and Robustness in Machine Learning [ICML'20]
• Automated Machine Learning [ICML'20]
• Interdisciplinary Problems (e.g., electronic health records and medical image)

Selected Publications

• B. Han, G. Niu, X. Yu, Q. Yao, M. Xu, I.W. Tsang, and M. Sugiyama. SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. In Proceedings of 37th International Conference on Machine Learning (ICML'20), 2020.
• V. Tangkaratt, B. Han, M. Khan, and M. Sugiyama. VILD: Variational Imitation Learning from Diverse-quality Demonstrations. In Proceedings of 37th International Conference on Machine Learning (ICML'20), 2020.
• B. Han, I.W. Tsang, X. Xiao, L. Chen, S.F. Fung, and C.P. Yu. Privacy-preserving Stochastic Gradual Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
• J. Zhang, B. Han, L. Wynter, B. Low, and M. Kankanhalli. Towards Robust ResNet: A Small Step but A Giant Leap. In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI'19), 2019.
• B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I.W. Tsang, and M. Sugiyama. Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. In Advances in Neural Information Processing Systems (NeurIPS'18), 2018.
• B. Han, J. Yao, G. Niu, M. Zhou, I.W. Tsang, Y. Zhang, and M. Sugiyama. Masking: A New Perspective of Noisy Supervision. In Advances in Neural Information Processing Systems (NeurIPS'18), 2018. 
• B. Han, Q. Yao, Y. Pan, I.W. Tsang, X. Xiao, Q. Yang, and M. Sugiyama. Millionaire: A Hint-guided Approach for Crowdsourcing. Machine Learning Journal (MLJ), 108(5): 831–858, 2018.
• Y. Pan, B. Han, and I.W. Tsang. Stagewise Learning for Noisy k-ary Preferences. Machine Learning Journal (MLJ), 107: 1333–1361, 2018.
• B. Han, Y. Pan, and I.W. Tsang. Robust Plackett-Luce Model for k-ary Crowdsourced Preferences. Machine Learning Journal (MLJ), 107(4): 675–702, 2017.