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Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack


Jun 15, 2022
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng


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Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation


Jun 06, 2022
De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama

* accepted by CVPR2022 

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Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients


Apr 07, 2022
Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

* ICLR 2022 camera-ready version 

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On the Effectiveness of Adversarial Training against Backdoor Attacks


Feb 22, 2022
Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama


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Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification


Feb 01, 2022
Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama


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PiCO: Contrastive Label Disambiguation for Partial Label Learning


Jan 29, 2022
Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao

* Accepted to ICLR 2022 (Oral Presentation, Acceptance Ratio: 1.6%) 

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Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations


Oct 22, 2021
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu


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Active Refinement for Multi-Label Learning: A Pseudo-Label Approach


Sep 29, 2021
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

* A preliminary version appeared in the Workshop on Learning from Limited Labeled Data @ ICLR 2019 

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Instance-dependent Label-noise Learning under a Structural Causal Model


Sep 12, 2021
Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang


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