Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (CoRR, 2017)

Classification from Pairwise Similarity and Unlabeled Data. - Han Bao 002; Gang Niu 001; Masashi Sugiyama (ICML, 2018)

Convex formulation of multiple instance learning from positive and unlabeled bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (Neural Networks, 2018)

Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. - Seiichi Kuroki; Nontawat Charoenphakdee; Han Bao 002; Junya Honda; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Imitation Learning from Imperfect Demonstration. - Yueh-Hua Wu; Nontawat Charoenphakdee; Han Bao 002; Voot Tangkaratt; Masashi Sugiyama (ICML, 2019)

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. - Han Bao 002; Masashi Sugiyama (AISTATS, 2020)

Calibrated Surrogate Losses for Adversarially Robust Classification. - Han Bao 002; Clayton Scott; Masashi Sugiyama (COLT, 2020)

Calibrated Surrogate Maximization of Dice. - Marcus Nordström; Han Bao 002; Fredrik Löfman; Henrik Hult; Atsuto Maki; Masashi Sugiyama (MICCAI (4), 2020)

Similarity-based Classification: Connecting Similarity Learning to Binary Classification. - Han Bao 002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama (CoRR, 2020)

Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. - Han Bao 002; Masashi Sugiyama (AISTATS, 2021)

Learning from Noisy Similar and Dissimilar Data. - Soham Dan; Han Bao 002; Masashi Sugiyama (ECML/PKDD (2), 2021)

Sharp Learning Bounds for Contrastive Unsupervised Representation Learning. - Han Bao 002; Yoshihiro Nagano; Kento Nozawa (CoRR, 2021)

Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. - Takuya Shimada; Han Bao 002; Issei Sato; Masashi Sugiyama (Neural Comput., 2021)

Robust computation of optimal transport by β-potential regularization. - Shintaro Nakamura; Han Bao 002; Masashi Sugiyama (ACML, 2022)

Pairwise Supervision Can Provably Elicit a Decision Boundary. - Han Bao 002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama (AISTATS, 2022)

On the Surrogate Gap between Contrastive and Supervised Losses. - Han Bao 002; Yoshihiro Nagano; Kento Nozawa (ICML, 2022)

Sparse Regularized Optimal Transport with Deformed q-Entropy. - Han Bao 002; Shinsaku Sakaue (Entropy, 2022)

Approximating 1-Wasserstein Distance with Trees. - Makoto Yamada; Yuki Takezawa; Ryoma Sato; Han Bao 002; Zornitsa Kozareva; Sujith Ravi (Trans. Mach. Learn. Res., 2022)

Unbalanced Optimal Transport for Unbalanced Word Alignment. - Yuki Arase; Han Bao 002; Sho Yokoi (ACL (1), 2023)

Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds. - Han Bao 002 (COLT, 2023)

Will Large-scale Generative Models Corrupt Future Datasets? - Ryuichiro Hataya; Han Bao 002; Hiromi Arai (ICCV, 2023)

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence. - Yuki Takezawa; Ryoma Sato; Han Bao 002; Kenta Niwa; Makoto Yamada (NeurIPS, 2023)

Estimating Treatment Effects Under Heterogeneous Interference. - Xiaofeng Lin 001; Guoxi Zhang; Xiaotian Lu; Han Bao 002; Koh Takeuchi 001; Hisashi Kashima (ECML/PKDD (1), 2023)

Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics. - Han Bao 002 (CoRR, 2023)

Embarrassingly Simple Text Watermarks. - Ryoma Sato; Yuki Takezawa; Han Bao 002; Kenta Niwa; Makoto Yamada (CoRR, 2023)

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data. - Yuki Takezawa; Han Bao 002; Kenta Niwa; Ryoma Sato; Makoto Yamada (Trans. Mach. Learn. Res., 2023)

Fast 1-Wasserstein distance approximations using greedy strategies. - Guillaume Houry; Han Bao 002; Han Zhao 002; Makoto Yamada (AISTATS, 2024)

Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. - Shinsaku Sakaue; Han Bao 002; Taira Tsuchiya; Taihei Oki (COLT, 2024)

Self-attention Networks Localize When QK-eigenspectrum Concentrates. - Han Bao 002; Ryuichiro Hataya; Ryo Karakida (ICML, 2024)

Parameter-free Clipped Gradient Descent Meets Polyak. - Yuki Takezawa; Han Bao 002; Ryoma Sato; Kenta Niwa; Makoto Yamada (NeurIPS, 2024)

Zipfian Whitening. - Sho Yokoi; Han Bao 002; Hiroto Kurita; Hidetoshi Shimodaira (NeurIPS, 2024)

Online Policy Learning from Offline Preferences. - Guoxi Zhang; Han Bao 002; Hisashi Kashima (CoRR, 2024)

Polyak Meets Parameter-free Clipped Gradient Descent. - Yuki Takezawa; Han Bao 002; Ryoma Sato; Kenta Niwa; Makoto Yamada (CoRR, 2024)

Calm Composite Losses: Being Improper Yet Proper Composite. - Han Bao 002; Nontawat Charoenphakdee (AISTATS, 2025)

Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States. - Han Bao 002; Shinsaku Sakaue (AISTATS, 2025)

Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. - Shinsaku Sakaue; Han Bao 002; Taira Tsuchiya (AISTATS, 2025)

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis. - Satoki Ishikawa; Makoto Yamada; Han Bao 002; Yuki Takezawa (ICLR, 2025)

Necessary and Sufficient Watermark for Large Language Models. - Yuki Takezawa; Ryoma Sato; Han Bao 002; Kenta Niwa; Makoto Yamada (Trans. Mach. Learn. Res., 2025)

Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis. - Shinsaku Sakaue; Taira Tsuchiya; Han Bao 002; Taihei Oki (CoRR, 2025)

Scalable individual treatment effect estimator for large graphs. - Xiaofeng Lin 001; Han Bao 002; Yan Cui 008; Koh Takeuchi 001; Hisashi Kashima (Mach. Learn., 2025)

Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses. - Han Bao 002; Shinsaku Sakaue; Yuki Takezawa (CoRR, 2025)

Many-to-Many Matching via Sparsity Controlled Optimal Transport. - Weijie Liu 006; Han Bao 002; Makoto Yamada; Zenan Huang; Nenggan Zheng; Hui Qian 001 (CoRR, 2025)