Han Bao

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 47

Top Topics

Multiple Instance

Binary Classification

Top Venues

Publications

Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. CoRR (2024)
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 (2024)
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)
Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada
Polyak Meets Parameter-free Clipped Gradient Descent. CoRR (2024)
Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada
Fast 1-Wasserstein distance approximations using greedy strategies. AISTATS (2024)
Han Bao, Ryuichiro Hataya, Ryo Karakida
Self-attention Networks Localize When QK-eigenspectrum Concentrates. CoRR (2024)
Guoxi Zhang, Han Bao, Hisashi Kashima
Online Policy Learning from Offline Preferences. CoRR (2024)
Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada
Embarrassingly Simple Text Watermarks. CoRR (2023)
Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada
Necessary and Sufficient Watermark for Large Language Models. CoRR (2023)
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)
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)
Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima
Estimating Treatment Effects Under Heterogeneous Interference. CoRR (2023)
Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. CoRR (2023)
Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima
Estimating Treatment Effects Under Heterogeneous Interference. ECML/PKDD (1) (2023)
Han Bao
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics. CoRR (2023)
Han Bao
Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds. COLT (2023)
Ryuichiro Hataya, Han Bao, Hiromi Arai
Will Large-scale Generative Models Corrupt Future Datasets? ICCV (2023)
Yuki Arase, Han Bao, Sho Yokoi
Unbalanced Optimal Transport for Unbalanced Word Alignment. ACL (1) (2023)
Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi
Approximating 1-Wasserstein Distance with Trees. CoRR (2022)
Shintaro Nakamura, Han Bao, Masashi Sugiyama
Robust computation of optimal transport by β-potential regularization. CoRR (2022)
Shintaro Nakamura, Han Bao, Masashi Sugiyama
Robust computation of optimal transport by β-potential regularization. ACML (2022)
Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. CoRR (2022)
Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS (2022)
Han Bao, Shinsaku Sakaue
Sparse Regularized Optimal Transport with Deformed q-Entropy. Entropy 24 (11) (2022)
Han Bao, Yoshihiro Nagano, Kento Nozawa
On the Surrogate Gap between Contrastive and Supervised Losses. ICML (2022)
Ryuichiro Hataya, Han Bao, Hiromi Arai
Will Large-scale Generative Models Corrupt Future Datasets? CoRR (2022)
Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi
Approximating 1-Wasserstein Distance with Trees. Trans. Mach. Learn. Res. 2022 (2022)
Han Bao, Yoshihiro Nagano, Kento Nozawa
Sharp Learning Bounds for Contrastive Unsupervised Representation Learning. CoRR (2021)
Soham Dan, Han Bao, Masashi Sugiyama
Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) (2021)
Han Bao, Masashi Sugiyama
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS (2021)
Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33 (5) (2021)
Marcus Nordström, Han Bao, Fredrik Löfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama
Calibrated Surrogate Maximization of Dice. MICCAI (4) (2020)
Han Bao, Clayton Scott, Masashi Sugiyama
Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR (2020)
Han Bao, Clayton Scott, Masashi Sugiyama
Calibrated Surrogate Losses for Adversarially Robust Classification. COLT (2020)
Soham Dan, Han Bao, Masashi Sugiyama
Learning from Noisy Similar and Dissimilar Data. CoRR (2020)
Han Bao, Masashi Sugiyama
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. AISTATS (2020)
Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR (2020)
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI (2019)
Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR (2019)
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
Imitation Learning from Imperfect Demonstration. CoRR (2019)
Han Bao, Masashi Sugiyama
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. CoRR (2019)
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
Imitation Learning from Imperfect Demonstration. ICML (2019)
Han Bao, Gang Niu, Masashi Sugiyama
Classification from Pairwise Similarity and Unlabeled Data. CoRR (2018)
Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105 (2018)
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR (2018)
Han Bao, Gang Niu, Masashi Sugiyama
Classification from Pairwise Similarity and Unlabeled Data. ICML (2018)
Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR (2017)