Han Bao - RankMe

65 papers A* 13A 7C 1Journal 40Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Evaluating Cross-Modal Reasoning Ability and Problem Characteristics with Multimodal Item Response Theory. CoRR Shunki Uebayashi, Kento Masui, Kyohei Atarashi, Han Bao, Hisashi Kashima, Naoto Inoue, Mayu Otani, Koh Takeuchi
2026	J	`jnl`	Modeling Dynamic Interference for Treatment Effect Estimation from Dynamic Graphs. ACM Trans. Knowl. Discov. Data Xiaofeng Lin, Han Bao, Koh Takeuchi, Yan Cui, Hisashi Kashima
2025	J	`jnl`	Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses. CoRR Han Bao, Shinsaku Sakaue, Yuki Takezawa
2025	A	`conf`	Calm Composite Losses: Being Improper Yet Proper Composite. AISTATS Han Bao, Nontawat Charoenphakdee
2025	A	`conf`	Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States. AISTATS Han Bao, Shinsaku Sakaue
2025	J	`jnl`	Many-to-Many Matching via Sparsity Controlled Optimal Transport. CoRR Weijie Liu, Han Bao, Makoto Yamada, Zenan Huang, Nenggan Zheng, Hui Qian
2025	J	`jnl`	Necessary and Sufficient Watermark for Large Language Models. Trans. Mach. Learn. Res. Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada
2025	J	`jnl`	Non-Stationary Online Structured Prediction with Surrogate Losses. CoRR Shinsaku Sakaue, Han Bao, Yuzhou Cao
2025	J	`jnl`	Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis. CoRR Shinsaku Sakaue, Taira Tsuchiya, Han Bao, Taihei Oki
2025	A*	`conf`	PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. ICLR Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa
2025	A	`conf`	Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. AISTATS Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2025	J	`jnl`	Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. CoRR Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2025	J	`jnl`	Scalable individual treatment effect estimator for large graphs. Mach. Learn. Xiaofeng Lin, Han Bao, Yan Cui, Koh Takeuchi, Hisashi Kashima
2024	A	`conf`	Fast 1-Wasserstein distance approximations using greedy strategies. AISTATS Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada
2024	J	`jnl`	Online Policy Learning from Offline Preferences. CoRR Guoxi Zhang, Han Bao, Hisashi Kashima
2024	A*	`conf`	Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. COLT Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
2024	J	`jnl`	Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. CoRR Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
2024	A*	`conf`	Parameter-free Clipped Gradient Descent Meets Polyak. NeurIPS Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada
2024	J	`jnl`	PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. CoRR Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa
2024	J	`jnl`	Polyak Meets Parameter-free Clipped Gradient Descent. CoRR Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada
2024	A*	`conf`	Self-attention Networks Localize When QK-eigenspectrum Concentrates. ICML Han Bao, Ryuichiro Hataya, Ryo Karakida
2024	J	`jnl`	Self-attention Networks Localize When QK-eigenspectrum Concentrates. CoRR Han Bao, Ryuichiro Hataya, Ryo Karakida
2024	A*	`conf`	Zipfian Whitening. NeurIPS Sho Yokoi, Han Bao, Hiroto Kurita, Hidetoshi Shimodaira
2024	J	`jnl`	Zipfian Whitening. CoRR Sho Yokoi, Han Bao, Hiroto Kurita, Hidetoshi Shimodaira
2023	A*	`conf`	Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. NeurIPS Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada
2023	J	`jnl`	Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. CoRR Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada
2023	J	`jnl`	Embarrassingly Simple Text Watermarks. CoRR Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada
2023	—	`conf`	Estimating Treatment Effects Under Heterogeneous Interference. ECML/PKDD (1) Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima
2023	J	`jnl`	Estimating Treatment Effects Under Heterogeneous Interference. CoRR Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima
2023	J	`jnl`	Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics. CoRR Han Bao
2023	J	`jnl`	Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. Trans. Mach. Learn. Res. Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada
2023	J	`jnl`	Necessary and Sufficient Watermark for Large Language Models. CoRR Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada
2023	A*	`conf`	Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds. COLT Han Bao
2023	—	`conf`	Unbalanced Optimal Transport for Unbalanced Word Alignment. ACL (1) Yuki Arase, Han Bao, Sho Yokoi
2023	J	`jnl`	Unbalanced Optimal Transport for Unbalanced Word Alignment. CoRR Yuki Arase, Han Bao, Sho Yokoi
2023	A*	`conf`	Will Large-scale Generative Models Corrupt Future Datasets? ICCV Ryuichiro Hataya, Han Bao, Hiromi Arai
2022	J	`jnl`	Approximating 1-Wasserstein Distance with Trees. CoRR Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi
2022	J	`jnl`	Approximating 1-Wasserstein Distance with Trees. Trans. Mach. Learn. Res. Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi
2022	J	`jnl`	Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. CoRR Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada
2022	A*	`conf`	On the Surrogate Gap between Contrastive and Supervised Losses. ICML Han Bao, Yoshihiro Nagano, Kento Nozawa
2022	A	`conf`	Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
2022	C	`conf`	Robust computation of optimal transport by β-potential regularization. ACML Shintaro Nakamura, Han Bao, Masashi Sugiyama
2022	J	`jnl`	Robust computation of optimal transport by β-potential regularization. CoRR Shintaro Nakamura, Han Bao, Masashi Sugiyama
2022	J	`jnl`	Sparse Regularized Optimal Transport with Deformed q-Entropy. Entropy Han Bao, Shinsaku Sakaue
2022	J	`jnl`	Will Large-scale Generative Models Corrupt Future Datasets? CoRR Ryuichiro Hataya, Han Bao, Hiromi Arai
2021	J	`jnl`	Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
2021	A	`conf`	Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS Han Bao, Masashi Sugiyama
2021	—	`conf`	Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) Soham Dan, Han Bao, Masashi Sugiyama
2021	J	`jnl`	Sharp Learning Bounds for Contrastive Unsupervised Representation Learning. CoRR Han Bao, Yoshihiro Nagano, Kento Nozawa
2020	A*	`conf`	Calibrated Surrogate Losses for Adversarially Robust Classification. COLT Han Bao, Clayton Scott, Masashi Sugiyama
2020	J	`jnl`	Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR Han Bao, Clayton Scott, Masashi Sugiyama
2020	—	`conf`	Calibrated Surrogate Maximization of Dice. MICCAI (4) Marcus Nordström, Han Bao, Fredrik Löfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama
2020	A	`conf`	Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. AISTATS Han Bao, Masashi Sugiyama
2020	J	`jnl`	Learning from Noisy Similar and Dissimilar Data. CoRR Soham Dan, Han Bao, Masashi Sugiyama
2020	J	`jnl`	Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
2019	J	`jnl`	Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. CoRR Han Bao, Masashi Sugiyama
2019	J	`jnl`	Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
2019	A*	`conf`	Imitation Learning from Imperfect Demonstration. ICML Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
2019	J	`jnl`	Imitation Learning from Imperfect Demonstration. CoRR Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
2019	A*	`conf`	Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
2018	A*	`conf`	Classification from Pairwise Similarity and Unlabeled Data. ICML Han Bao, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Classification from Pairwise Similarity and Unlabeled Data. CoRR Han Bao, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
2017	J	`jnl`	Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama