Han Bao

Orcid: 0000-0002-4473-2604

Affiliations:
  • Kyoto University, Japan
  • The University of Tokyo, Tokyo, Japan (former)


According to our database1, Han Bao authored at least 30 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
Online Policy Learning from Offline Preferences.
CoRR, 2024

Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss.
CoRR, 2024

Self-attention Networks Localize When QK-eigenspectrum Concentrates.
CoRR, 2024

2023
Embarrassingly Simple Text Watermarks.
CoRR, 2023

Necessary and Sufficient Watermark for Large Language Models.
CoRR, 2023

Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics.
CoRR, 2023

Estimating Treatment Effects Under Heterogeneous Interference.
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.
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?
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Unbalanced Optimal Transport for Unbalanced Word Alignment.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
Approximating 1-Wasserstein Distance with Trees.
Trans. Mach. Learn. Res., 2022

Sparse Regularized Optimal Transport with Deformed q-Entropy.
Entropy, 2022

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data.
CoRR, 2022

On the Surrogate Gap between Contrastive and Supervised Losses.
Proceedings of the International Conference on Machine Learning, 2022

Pairwise Supervision Can Provably Elicit a Decision Boundary.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Robust computation of optimal transport by β-potential regularization.
Proceedings of the Asian Conference on Machine Learning, 2022

2021
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.
Neural Comput., 2021

Sharp Learning Bounds for Contrastive Unsupervised Representation Learning.
CoRR, 2021

Learning from Noisy Similar and Dissimilar Data.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Similarity-based Classification: Connecting Similarity Learning to Binary Classification.
CoRR, 2020

Calibrated Surrogate Maximization of Dice.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Calibrated Surrogate Losses for Adversarially Robust Classification.
Proceedings of the Conference on Learning Theory, 2020

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Imitation Learning from Imperfect Demonstration.
Proceedings of the 36th International Conference on Machine Learning, 2019

Unsupervised Domain Adaptation Based on Source-Guided Discrepancy.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Convex formulation of multiple instance learning from positive and unlabeled bags.
Neural Networks, 2018

Classification from Pairwise Similarity and Unlabeled Data.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags.
CoRR, 2017


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