Han Bao

According to our database, Han Bao authored at least 34 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Zipfian Whitening.

Sho Yokoi, Han Bao, Hiroto Kurita, Hidetoshi Shimodaira

CoRR, 2024

Polyak Meets Parameter-free Clipped Gradient Descent.

Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada

CoRR, 2024

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis.

Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa

CoRR, 2024

Online Policy Learning from Offline Preferences.

Guoxi Zhang, Han Bao, Hisashi Kashima

CoRR, 2024

Self-attention Networks Localize When QK-eigenspectrum Concentrates.

Han Bao, Ryuichiro Hataya, Ryo Karakida

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss.

Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Fast 1-Wasserstein distance approximations using greedy strategies.

Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data.

Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada

Trans. Mach. Learn. Res., 2023

Embarrassingly Simple Text Watermarks.

Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa, Makoto Yamada

CoRR, 2023

Necessary and Sufficient Watermark for Large Language Models.

Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada

CoRR, 2023

Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics.

Han Bao

CoRR, 2023

Estimating Treatment Effects Under Heterogeneous Interference.

Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.

Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Will Large-scale Generative Models Corrupt Future Datasets?

Ryuichiro Hataya, Han Bao, Hiromi Arai

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds.

Han Bao

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Unbalanced Optimal Transport for Unbalanced Word Alignment.

Yuki Arase, Han Bao, Sho Yokoi

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Approximating 1-Wasserstein Distance with Trees.

Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi

Trans. Mach. Learn. Res., 2022

Sparse Regularized Optimal Transport with Deformed q-Entropy.

Han Bao, Shinsaku Sakaue

Entropy, 2022

On the Surrogate Gap between Contrastive and Supervised Losses.

Han Bao, Yoshihiro Nagano, Kento Nozawa

Proceedings of the International Conference on Machine Learning, 2022

Pairwise Supervision Can Provably Elicit a Decision Boundary.

Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Robust computation of optimal transport by β-potential regularization.

Shintaro Nakamura, Han Bao, Masashi Sugiyama

Proceedings of the Asian Conference on Machine Learning, 2022

2021

Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.

Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama

Neural Comput., 2021

Sharp Learning Bounds for Contrastive Unsupervised Representation Learning.

Han Bao, Yoshihiro Nagano, Kento Nozawa

CoRR, 2021

Learning from Noisy Similar and Dissimilar Data.

Soham Dan, Han Bao, Masashi Sugiyama

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation.

Han Bao, Masashi Sugiyama

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Similarity-based Classification: Connecting Similarity Learning to Binary Classification.

Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama

CoRR, 2020

Calibrated Surrogate Maximization of Dice.

Marcus Nordström, Han Bao, Fredrik Löfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Calibrated Surrogate Losses for Adversarially Robust Classification.

Han Bao, Clayton Scott, Masashi Sugiyama

Proceedings of the Conference on Learning Theory, 2020

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.

Han Bao, Masashi Sugiyama

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Imitation Learning from Imperfect Demonstration.

Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama

Proceedings of the 36th International Conference on Machine Learning, 2019

Unsupervised Domain Adaptation Based on Source-Guided Discrepancy.

Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Convex formulation of multiple instance learning from positive and unlabeled bags.

Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama

Neural Networks, 2018

Classification from Pairwise Similarity and Unlabeled Data.

Han Bao, Gang Niu, Masashi Sugiyama

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags.

Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama

CoRR, 2017