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Gang Niu 0001
Person information

- affiliation: RIKEN, Japan
- affiliation (former, PhD 2013): Tokyo Institute of Technology, Department of Computer Science, Japan
- affiliation (former): Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China
Other persons with the same name
- Gang Niu 0002 — City University of Hong Kong, Center for Prognostics and System Health Management, Hong Kong (and 1 more)
- Gang Niu 0003 — First Affiliated Hospital of Xi'an Jiaotong University, Department of Radiology, Xi'an, China
- Gang Niu 0004
— Tongji University, Institute of Rail Transit, Shanghai, China
- Gang Niu 0005 — Zhengzhou Huali Information Technology Co., Ltd., China
- Gang Niu 0006 — Xi'an Jiaotong University, International Center for Dielectric Research, China
- Gang Niu 0007 — Sun Yat-sen University, First Affiliated Hospital, Department of Obstetrics and Gynecology, Guangzhou, China
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2020 – today
- 2022
- [j15]Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama:
Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. 23: 23:1-23:35 (2022) - [i80]Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao:
PiCO: Contrastive Label Disambiguation for Partial Label Learning. CoRR abs/2201.08984 (2022) - [i79]Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR abs/2202.00395 (2022) - [i78]Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama:
On the Effectiveness of Adversarial Training against Backdoor Attacks. CoRR abs/2202.10627 (2022) - [i77]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR abs/2204.03304 (2022) - [i76]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i75]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. CoRR abs/2206.07314 (2022) - 2021
- [j14]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33(1): 244-268 (2021) - [j13]Wenkai Xu, Gang Niu, Aapo Hyvärinen
, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. 33(8): 2128-2162 (2021) - [c64]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI 2021: 10183-10191 - [c63]Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama:
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL 2021: 581-592 - [c62]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. ICLR 2021 - [c61]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c60]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. ICML 2021: 1272-1282 - [c59]Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama:
Large-Margin Contrastive Learning with Distance Polarization Regularizer. ICML 2021: 1673-1683 - [c58]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c57]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu
, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML 2021: 3252-3262 - [c56]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c55]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c54]Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML 2021: 7134-7144 - [c53]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. ICML 2021: 11285-11295 - [c52]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. ICML 2021: 11693-11703 - [c51]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML 2021: 12501-12512 - [c50]Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang
, Bo An, Gang Niu:
Multiple-Instance Learning from Similar and Dissimilar Bags. KDD 2021: 374-382 - [c49]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS 2021: 4409-4420 - [c48]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c47]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. NeurIPS 2021: 24392-24403 - [i74]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR abs/2101.05467 (2021) - [i73]Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR abs/2102.00678 (2021) - [i72]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i71]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i70]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR abs/2102.02414 (2021) - [i69]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i68]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR abs/2102.04002 (2021) - [i67]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. CoRR abs/2102.05311 (2021) - [i66]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. CoRR abs/2102.06879 (2021) - [i65]Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama:
Guided Interpolation for Adversarial Training. CoRR abs/2102.07327 (2021) - [i64]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Label-noise Transition Matrix using DNNs. CoRR abs/2105.13001 (2021) - [i63]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - [i62]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i61]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i60]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu:
Understanding (Generalized) Label Smoothing when Learning with Noisy Labels. CoRR abs/2106.04149 (2021) - [i59]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. CoRR abs/2106.04928 (2021) - [i58]Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. CoRR abs/2106.06152 (2021) - [i57]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - [i56]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i55]Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Multi-Class Classification from Single-Class Data with Confidences. CoRR abs/2106.08864 (2021) - [i54]Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng:
Local Reweighting for Adversarial Training. CoRR abs/2106.15776 (2021) - [i53]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. CoRR abs/2106.15853 (2021) - [i52]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR abs/2109.02986 (2021) - [i51]Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR abs/2109.14676 (2021) - [i50]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. CoRR abs/2110.12088 (2021) - 2020
- [c46]Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao:
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. AAAI 2020: 4602-4609 - [c45]Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. AISTATS 2020: 1115-1125 - [c44]Chun Wang, Bo Han, Shirui Pan, Jing Jiang, Gang Niu, Guodong Long:
Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. ICDM 2020: 571-580 - [c43]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. ICML 2020: 1929-1938 - [c42]Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama:
Learning with Multiple Complementary Labels. ICML 2020: 3072-3081 - [c41]Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML 2020: 4006-4016 - [c40]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020: 4604-4614 - [c39]Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. ICML 2020: 6500-6510 - [c38]Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok:
Searching to Exploit Memorization Effect in Learning with Noisy Labels. ICML 2020: 10789-10798 - [c37]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. ICML 2020: 11278-11287 - [c36]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Rethinking Importance Weighting for Deep Learning under Distribution Shift. NeurIPS 2020 - [c35]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. NeurIPS 2020 - [c34]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS 2020 - [c33]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. NeurIPS 2020 - [i49]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. CoRR abs/2001.03772 (2020) - [i48]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Towards Mixture Proportion Estimation without Irreducibility. CoRR abs/2002.03673 (2020) - [i47]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Multi-Class Classification from Noisy-Similarity-Labeled Data. CoRR abs/2002.06508 (2020) - [i46]Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. CoRR abs/2002.08053 (2020) - [i45]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR abs/2002.08709 (2020) - [i44]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CoRR abs/2002.11242 (2020) - [i43]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Rethinking Importance Weighting for Deep Learning under Distribution Shift. CoRR abs/2006.04662 (2020) - [i42]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. CoRR abs/2006.07805 (2020) - [i41]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A New Perspective on Learning with Label Noise. CoRR abs/2006.07831 (2020) - [i40]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR abs/2006.07836 (2020) - [i39]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. CoRR abs/2007.02235 (2020) - [i38]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. CoRR abs/2007.08929 (2020) - [i37]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. CoRR abs/2010.01736 (2020) - [i36]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu
, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. CoRR abs/2010.01875 (2020) - [i35]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR abs/2010.11415 (2020) - [i34]Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama:
A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR abs/2011.04406 (2020) - [i33]Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long:
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. CoRR abs/2012.00925 (2020)
2010 – 2019
- 2019
- [c32]Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. ICLR (Poster) 2019 - [c31]Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama:
Classification from Positive, Unlabeled and Biased Negative Data. ICML 2019: 2820-2829 - [c30]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. ICML 2019: 2971-2980 - [c29]Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? ICML 2019: 7164-7173 - [c28]Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama:
Uncoupled Regression from Pairwise Comparison Data. NeurIPS 2019: 3994-4004 - [c27]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS 2019: 6835-6846 - [i32]Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? CoRR abs/1901.04215 (2019) - [i31]Miao Xu
, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama:
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. CoRR abs/1901.10155 (2019) - [i30]Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama:
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR abs/1905.07720 (2019) - [i29]Yuangang Pan, Weijie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
Fast and Robust Rank Aggregation against Model Misspecification. CoRR abs/1905.12341 (2019) - [i28]Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama:
Uncoupled Regression from Pairwise Comparison Data. CoRR abs/1905.13659 (2019) - [i27]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR abs/1906.00189 (2019) - [i26]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR abs/1907.09588 (2019) - [i25]Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. CoRR abs/1910.08974 (2019) - [i24]Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama:
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. CoRR abs/1910.13339 (2019) - [i23]Hansi Yang, Quanming Yao, Bo Han, Gang Niu:
Searching to Exploit Memorization Effect in Learning from Corrupted Labels. CoRR abs/1911.02377 (2019) - [i22]Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? CoRR abs/1911.08696 (2019) - 2018
- [j12]Tomoya Sakai, Gang Niu, Masashi Sugiyama
:
Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107(4): 767-794 (2018) - [j11]Tomoya Sakai, Gang Niu, Masashi Sugiyama
:
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107(4): 795 (2018) - [j10]Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama
:
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. Neural Comput. 30(2) (2018) - [c26]Han Bao
, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. ICML 2018: 461-470 - [c25]Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama:
Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML 2018: 2034-2042 - [c24]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama
, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. KDD 2018: 1571-1579 - [c23]Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. NeurIPS 2018: 5841-5851 - [c22]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Binary Classification from Positive-Confidence Data. NeurIPS 2018: 5921-5932 - [c21]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS 2018: 8536-8546 - [i21]Han Bao, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. CoRR abs/1802.04381 (2018) - [i20]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. CoRR abs/1802.05380 (2018) - [i19]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-sampling: Training Robust Networks for Extremely Noisy Supervision. CoRR abs/1804.06872 (2018) - [i18]Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. CoRR abs/1805.08193 (2018) - [i17]Miao Xu
, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama:
Matrix Co-completion for Multi-label Classification with Missing Features and Labels. CoRR abs/1805.09156 (2018) - [i16]Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. CoRR abs/1808.10585 (2018) - [i15]Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama:
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data. CoRR abs/1809.05710 (2018) - [i14]Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. CoRR abs/1809.11008 (2018) - [i13]Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama:
Classification from Positive, Unlabeled and Biased Negative Data. CoRR abs/1810.00846 (2018) - [i12]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. CoRR abs/1810.04327 (2018) - 2017
- [j9]Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama:
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. 18: 180:1-180:47 (2017) - [j8]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
:
Class-prior estimation for learning from positive and unlabeled data. Mach. Learn. 106(4): 463-492 (2017) - [c20]Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama:
Whitening-Free Least-Squares Non-Gaussian Component Analysis. ACML 2017: 375-390 - [c19]Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML 2017: 2998-3006 - [c18]Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Positive-Unlabeled Learning with Non-Negative Risk Estimator. NIPS 2017: 1675-1685 - [c17]Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama:
Learning from Complementary Labels. NIPS 2017: 5639-5649 - [i11]