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ORCIDHan Bao 0002
Person information
- affiliation: The University of Tokyo, Tokyo, Japan
Other persons with the same name
- Han Bao 0001
![[0000-0002-2329-6890]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-2329-6890")
— Nanjing University of Aeronautics and Astronautics, Nanjing, China (and 1 more) - Han Bao 0003 — University of Iowa, Iowa City, IA, USA
- Han Bao 0004 — Idaho National Laboratory, Idaho Falls, ID, USA
- Han Bao 0005 — Beihang University, Beijing, China
- Han Bao 0006 — National University of Defense Technology, Changsha, China
- Han Bao 0007 — Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China
- Han Bao 0008 — Chinese Academy of Sciences, Key Laboratory of Wireless Sensor Networks and Communications, Shanghai, China
- Han Bao 0009 — King's College London, London, UK
- Han Bao 0010 — University of Nottingham Ningbo China, Ningbo, China
- Han Bao 0011 — Tsinghua University, Beijing, China
- Han Bao 0012 — Zhejiang Ocean University, School of Mathematics, Physics and Information Science, Zhoushan, China
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2020 – today
- 2021
[c7]Han Bao, Masashi Sugiyama:
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS 2021: 1648-1656- 2020
- [c6]Han Bao, Masashi Sugiyama:
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. AISTATS 2020: 2337-2347 - [c5]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. COLT 2020: 408-451 - [c4]Marcus Nordström, Han Bao, Fredrik Löfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama:
Calibrated Surrogate Maximization of Dice. MICCAI (4) 2020: 269-278 - [i9]Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. CoRR abs/2002.00995 (2020) - [i8]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR abs/2005.13748 (2020) - [i7]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR abs/2006.06207 (2020)
2010 – 2019
- 2019
- [c3]Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama:
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI 2019: 4122-4129 - [c2]Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. ICML 2019: 6818-6827 - [i6]Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. CoRR abs/1901.09387 (2019) - [i5]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR abs/1904.11717 (2019) - [i4]Han Bao, Masashi Sugiyama:
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. CoRR abs/1905.12511 (2019) - 2018
- [j1]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105: 132-141 (2018) - [c1]Han Bao, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. ICML 2018: 461-470 - [i3]Han Bao, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. CoRR abs/1802.04381 (2018) - [i2]Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama:
Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR abs/1809.03839 (2018) - 2017
- [i1]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR abs/1704.06767 (2017)
Coauthor Index
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