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ORCIDHan Bao 0002
Person information
- affiliation: Kyoto University, Japan
- affiliation (former): The University of Tokyo, Tokyo, Japan
Other persons with the same name
- Han Bao — disambiguation page
- 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
- Han Bao 0013 — Huazhong University of Science and Technology, School of Optical and Electronic Information, Wuhan, China
- Han Bao 0014 — Yunnan Minzu University, School of Electrical and Information Engineering, Kunming, China
- Han Bao 0015 — University of Chinese Academy of Sciences, School of Electronic, Electrical and Communication Engineering, Beijing, China
- Han Bao 0016 — East China Normal University, Center for Modern Chinese City Studies, Shanghai, China
- Han Bao 0017 — Harbin Engineering University, College of Mechanical and Electrical Engineering, China
- Han Bao 0018 — NVIDIA, China
- Han Bao 0019 — Chinese Academy of Sciences, Dalian Institute of Chemical Physics, China
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2020 – today
- 2024
[c19]Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada:
Fast 1-Wasserstein distance approximations using greedy strategies. AISTATS 2024: 325-333- [c18]Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki:
Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. COLT 2024: 4458-4486 - [c17]Han Bao, Ryuichiro Hataya, Ryo Karakida:
Self-attention Networks Localize When QK-eigenspectrum Concentrates. ICML 2024 - [i25]Han Bao, Ryuichiro Hataya, Ryo Karakida:
Self-attention Networks Localize When QK-eigenspectrum Concentrates. CoRR abs/2402.02098 (2024) - [i24]Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki:
Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. CoRR abs/2402.08180 (2024) - [i23]Guoxi Zhang, Han Bao, Hisashi Kashima:
Online Policy Learning from Offline Preferences. CoRR abs/2403.10160 (2024) - [i22]Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa:
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. CoRR abs/2405.14650 (2024) - [i21]Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada:
Polyak Meets Parameter-free Clipped Gradient Descent. CoRR abs/2405.15010 (2024) - 2023
- [j5]Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada:
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. Trans. Mach. Learn. Res. 2023 (2023) - [c16]Yuki Arase, Han Bao, Sho Yokoi:
Unbalanced Optimal Transport for Unbalanced Word Alignment. ACL (1) 2023: 3966-3986 - [c15]Han Bao:
Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds. COLT 2023: 525-547 - [c14]Ryuichiro Hataya, Han Bao, Hiromi Arai:
Will Large-scale Generative Models Corrupt Future Datasets? ICCV 2023: 20498-20508 - [c13]Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. NeurIPS 2023 - [c12]Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima:
Estimating Treatment Effects Under Heterogeneous Interference. ECML/PKDD (1) 2023: 576-592 - [i20]Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. CoRR abs/2305.11420 (2023) - [i19]Yuki Arase, Han Bao, Sho Yokoi:
Unbalanced Optimal Transport for Unbalanced Word Alignment. CoRR abs/2306.04116 (2023) - [i18]Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima:
Estimating Treatment Effects Under Heterogeneous Interference. CoRR abs/2309.13884 (2023) - [i17]Han Bao:
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics. CoRR abs/2309.16109 (2023) - [i16]Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Necessary and Sufficient Watermark for Large Language Models. CoRR abs/2310.00833 (2023) - [i15]Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada:
Embarrassingly Simple Text Watermarks. CoRR abs/2310.08920 (2023) - 2022
- [j4]Han Bao, Shinsaku Sakaue:
Sparse Regularized Optimal Transport with Deformed q-Entropy. Entropy 24(11): 1634 (2022) - [j3]Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi:
Approximating 1-Wasserstein Distance with Trees. Trans. Mach. Learn. Res. 2022 (2022) - [c11]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. ACML 2022: 770-785 - [c10]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS 2022: 2618-2640 - [c9]Han Bao, Yoshihiro Nagano, Kento Nozawa:
On the Surrogate Gap between Contrastive and Supervised Losses. ICML 2022: 1585-1606 - [i14]Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi:
Approximating 1-Wasserstein Distance with Trees. CoRR abs/2206.12116 (2022) - [i13]Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada:
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2209.15505 (2022) - [i12]Ryuichiro Hataya, Han Bao, Hiromi Arai:
Will Large-scale Generative Models Corrupt Future Datasets? CoRR abs/2211.08095 (2022) - [i11]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. CoRR abs/2212.13251 (2022) - 2021
- [j2]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33(5): 1234-1268 (2021) - [c8]Han Bao, Masashi Sugiyama:
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS 2021: 1648-1656 - [c7]Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) 2021: 233-249 - [i10]Han Bao, Yoshihiro Nagano, Kento Nozawa:
Sharp Learning Bounds for Contrastive Unsupervised Representation Learning. CoRR abs/2110.02501 (2021) - 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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