Han Bao
Han Bao
PhD student, The University of Tokyo / RIKEN AIP
Verified email at ms.k.u-tokyo.ac.jp - Homepage
Cited by
Cited by
Classification from Pairwise Similarity and Unlabeled Data
H Bao, G Niu, M Sugiyama
ICML 2018, 2018
Imitation Learning from Imperfect Demonstration
YH Wu, N Charoenphakdee, H Bao, V Tangkaratt, M Sugiyama
ICML 2019, 2019
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
S Kuroki, N Charoenphakdee, H Bao, J Honda, I Sato, M Sugiyama
AAAI 2019, 2019
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
T Shimada, H Bao, I Sato, M Sugiyama
Neural Computation, 2021
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
H Bao, T Sakai, I Sato, M Sugiyama
Neural Networks 105, 132-141, 2018
Calibrated Surrogate Losses for Adversarially Robust Classification
H Bao, C Scott, M Sugiyama
COLT 2020, 2020
Similarity-based Classification: Connecting Similarity Learning to Binary Classification
H Bao, T Shimada, L Xu, I Sato, M Sugiyama
arXiv preprint arXiv:2006.06207, 2020
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
H Bao, M Sugiyama
AISTATS 2020, 2020
Calibrated Surrogate Maximization of Dice
M Nordström, H Bao, F Löfman, H Hult, A Maki, M Sugiyama
MICCAI 2020, 269-278, 2020
Learning from noisy similar and dissimilar data
S Dan, H Bao, M Sugiyama
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation
H Bao, M Sugiyama
International Conference on Artificial Intelligence and Statistics, 1648-1656, 2021
Sharp Learning Bounds for Contrastive Unsupervised Representation Learning
H Bao, Y Nagano, K Nozawa
arXiv preprint arXiv:2110.02501, 2021
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