Bo Han
Bo Han
HKBU / RIKEN
Verified email at comp.hkbu.edu.hk
Title
Cited by
Cited by
Year
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, IW Tsang, M Sugiyama
Advances in Neural Information Processing Systems, 2018
2072018
Masking: A New Perspective of Noisy Supervision
B Han, J Yao, G Niu, M Zhou, IW Tsang, Z Ya, M Sugiyama
Advances in Neural Information Processing Systems, 2018
532018
How does Disagreement Help Generalization against Label Corruption?
X Yu, B Han, J Yao, G Niu, IW Tsang, M Sugiyama
International Conference on Machine Learning, 2019
47*2019
Are Anchor Points Really Indispensable in Label-Noise Learning?
X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama
Advances in Neural Information Processing Systems, 2019
182019
Progressive Stochastic Learning for Noisy Labels
B Han, IW Tsang, L Chen, C Yu, SF Fung
IEEE Transactions on Neural Networks and Learning Systems, 2017
182017
Fast Image Recognition based on Independent Component Analysis
S Zhang, B He, R Nian, J Wang, B Han, A Lendasse, G Yuan
Cognitive Computation, 2014
172014
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent
B Han, IW Tsang, L Chen
European Conference on Machine Learning, 2016
132016
LARSEN: Selective Ensemble Learning using LARS for Blended Data
B Han, B He, R Nian, M Ma, S Zhang, M Li, A Lendasse
Neurocomputing, 2015
13*2015
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
Q Yao, JT Kwok, B Han
International Conference on Machine Learning, 2019
12*2019
Towards Robust ResNet: A Small Step but A Giant Leap
J Zhang, B Han, L Wynter, KH Low, M Kankanhalli
International Joint Conference on Artificial Intelligence, 2019
112019
Robust Plackett–Luce Model for k-ary Crowdsourced Preferences
B Han, Y Pan, IW Tsang
Machine Learning Journal, 2017
92017
HSR: L 1/2-regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection
B Han, B He, T Sun, T Yan, M Ma, Y Shen, A Lendasse
Neural Computing and Applications, 2015
82015
Matrix co-completion for multi-label classification with missing features and labels
M Xu, G Niu, B Han, IW Tsang, ZH Zhou, M Sugiyama
arXiv preprint arXiv:1805.09156, 2018
52018
Learning from Multiple Complementary Labels
L Feng, T Kaneko, B Han, G Niu, B An, M Sugiyama
International Conference on Machine Learning, 2020
42020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
A Berthon, B Han, G Niu, T Liu, M Sugiyama
arXiv preprint arXiv:2001.03772, 2020
32020
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation
F Liu, J Lu, B Han, G Niu, G Zhang, M Sugiyama
arXiv preprint arXiv:1905.07720 presented at NeurIPS19 workshop, 2019
32019
Stagewise Learning for Noisy k-ary Preferences
Y Pan, B Han, IW Tsang
Machine Learning Journal, 2017
32017
Dual T: Reducing estimation error for transition matrix in label-noise learning
Y Yao, T Liu, B Han, M Gong, J Deng, G Niu, M Sugiyama
arXiv preprint arXiv:2006.07805, 2020
12020
Parts-dependent label noise: Towards instance-dependent label noise
X Xia, T Liu, B Han, N Wang, M Gong, H Liu, G Niu, D Tao, M Sugiyama
arXiv preprint arXiv:2006.07836, 2020
12020
Multi-Class Classification from Noisy-Similarity-Labeled Data
S Wu, X Xia, T Liu, B Han, M Gong, N Wang, H Liu, G Niu
arXiv preprint arXiv:2002.06508, 2020
12020
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