


default search action
Gang Niu 0001
Person information
- affiliation: RIKEN, Japan
- affiliation (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 (and 1 more)
- Gang Niu 0007 — Sun Yat-sen University, First Affiliated Hospital, Department of Obstetrics and Gynecology, Guangzhou, China
- Gang Niu 0008 — Phil Rivers Technology Ltd, Beijing, China
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2025
- [j29]Wenshui Luo
, Shuo Chen
, Tongliang Liu
, Bo Han
, Gang Niu
, Masashi Sugiyama
, Dacheng Tao
, Chen Gong
:
Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(1): 305-322 (2025) - 2024
- [j28]Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen
, Ning Xie, Gang Niu, Masashi Sugiyama
:
Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks 180: 106741 (2024) - [j27]Jiaqi Lv
, Biao Liu
, Lei Feng
, Ning Xu
, Miao Xu
, Bo An
, Gang Niu
, Xin Geng
, Masashi Sugiyama
:
On the Robustness of Average Losses for Partial-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 2569-2583 (2024) - [j26]Haobo Wang
, Ruixuan Xiao
, Yixuan Li
, Lei Feng
, Gang Niu
, Gang Chen
, Junbo Zhao
:
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3183-3198 (2024) - [j25]Yinghua Gao
, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia
, Gang Niu
, Masashi Sugiyama
:
On the Effectiveness of Adversarial Training Against Backdoor Attacks. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14878-14888 (2024) - [c105]Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang, Gang Niu, Xiaofeng Zhu:
Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios. CVPR 2024: 22957-22966 - [c104]Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama:
Direct Distillation Between Different Domains. ECCV (80) 2024: 154-172 - [c103]Jiahao Xiao
, Ming-Kun Xie
, Heng-Bo Fan
, Gang Niu
, Masashi Sugiyama
, Sheng-Jun Huang
:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. ECCV (52) 2024: 437-454 - [c102]Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama:
Robust Similarity Learning with Difference Alignment Regularization. ICLR 2024 - [c101]Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama:
Accurate Forgetting for Heterogeneous Federated Continual Learning. ICLR 2024 - [c100]Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. ICML 2024 - [c99]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. ICML 2024 - [c98]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML 2024 - [c97]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. ICML 2024 - [c96]Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. ICML 2024 - [c95]Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang:
Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. NeurIPS 2024 - [c94]Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng:
What Makes Partial-Label Learning Algorithms Effective? NeurIPS 2024 - [i109]Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama:
Direct Distillation between Different Domains. CoRR abs/2401.06826 (2024) - [i108]Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. CoRR abs/2402.06918 (2024) - [i107]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR abs/2404.06287 (2024) - [i106]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. CoRR abs/2405.09892 (2024) - [i105]Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. CoRR abs/2405.18890 (2024) - [i104]Jianing Zhu, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama:
Decoupling the Class Label and the Target Concept in Machine Unlearning. CoRR abs/2406.08288 (2024) - [i103]Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR abs/2407.18624 (2024) - [i102]Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
On Unsupervised Prompt Learning for Classification with Black-box Language Models. CoRR abs/2410.03124 (2024) - [i101]Yuxin Tian, Mouxing Yang, Yuhao Zhou, Jian Wang, Qing Ye, Tongliang Liu, Gang Niu, Jiancheng Lv:
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels. CoRR abs/2412.00452 (2024) - 2023
- [j24]Shuo Chen
, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Boundary-restricted metric learning. Mach. Learn. 112(12): 4723-4762 (2023) - [j23]Tingting Zhao, S. Wu, G. Li, Y. Chen, Gang Niu, Masashi Sugiyama:
Learning Intention-Aware Policies in Deep Reinforcement Learning. Neural Comput. 35(10): 1657-1677 (2023) - [j22]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation learning for continuous action spaces is beneficial for efficient policy learning. Neural Networks 159: 137-152 (2023) - [j21]Chen Gong
, Yongliang Ding, Bo Han
, Gang Niu
, Jian Yang
, Jane You
, Dacheng Tao
, Masashi Sugiyama
:
Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2835-2848 (2023) - [j20]Shuo Yang
, Songhua Wu
, Erkun Yang
, Bo Han
, Yang Liu
, Min Xu
, Gang Niu
, Tongliang Liu
:
A Parametrical Model for Instance-Dependent Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14055-14068 (2023) - [j19]Lei Feng
, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei
, Tao Xiang
, Bo An
, Gang Niu
:
Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity. IEEE Trans. Knowl. Data Eng. 35(11): 11599-11609 (2023) - [c93]Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng:
Towards Effective Visual Representations for Partial-Label Learning. CVPR 2023: 15589-15598 - [c92]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. ICCV 2023: 17225-17234 - [c91]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. ICCV 2023: 17424-17434 - [c90]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. ICLR 2023 - [c89]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c88]Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. ICML 2023: 36804-36820 - [c87]Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li:
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. ICML 2023: 36868-36886 - [c86]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. NeurIPS 2023 - [c85]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. NeurIPS 2023 - [c84]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS 2023 - [c83]Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, Xiaofeng Zhu:
Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration. NeurIPS 2023 - [c82]Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS 2023 - [i100]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i99]Jie Xu, Gang Niu, Xiaolong Wang, Yazhou Ren, Lei Feng, Xiaoshuang Shi, Heng Tao Shen, Xiaofeng Zhu:
Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios. CoRR abs/2303.17245 (2023) - [i98]Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama:
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR abs/2305.00399 (2023) - [i97]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR abs/2305.02795 (2023) - [i96]Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng:
Towards Effective Visual Representations for Partial-Label Learning. CoRR abs/2305.06080 (2023) - [i95]Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR abs/2305.08344 (2023) - [i94]Tongtong Fang
, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR abs/2305.14690 (2023) - [i93]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i92]Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. CoRR abs/2306.11343 (2023) - [i91]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR abs/2307.05948 (2023) - [i90]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR abs/2307.11469 (2023) - [i89]Penghui Yang
, Ming-Kun Xie, Chen-Chen Zong, Lei Feng
, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. CoRR abs/2308.06453 (2023) - [i88]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. CoRR abs/2310.05632 (2023) - [i87]Wentao Yu, Shuo Chen, Chen Gong, Gang Niu, Masashi Sugiyama:
Atom-Motif Contrastive Transformer for Molecular Property Prediction. CoRR abs/2310.07351 (2023) - [i86]Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR abs/2310.13923 (2023) - [i85]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR abs/2311.15502 (2023) - 2022
- [j18]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) - [j17]Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama:
Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. 23: 307:1-307:34 (2022) - [j16]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. Trans. Mach. Learn. Res. 2022 (2022) - [j15]Jingfeng Zhang
, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [c81]Masashi Sugiyama, Tongliang Liu
, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c80]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. CVPR 2022: 16609-16618 - [c79]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou
, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c78]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. ICLR 2022 - [c77]Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao:
PiCO: Contrastive Label Disambiguation for Partial Label Learning. ICLR 2022 - [c76]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. ICLR 2022 - [c75]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. ICLR 2022 - [c74]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c73]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c72]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c71]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang
, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. ICLR 2022 - [c70]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. ICML 2022: 7144-7163 - [c69]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c68]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. ICML 2022: 25302-25312 - [c67]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS 2022 - [c66]Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Learning Contrastive Embedding in Low-Dimensional Space. NeurIPS 2022 - [c65]Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama:
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. NeurIPS 2022 - [e1]Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato:
International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research 162, PMLR 2022 [contents] - [i84]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) - [i83]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) - [i82]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) - [i81]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) - [i80]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) - [i79]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) - [i78]Qiong Zhang, Aline Talhouk, Gang Niu, Xiaoxiao Li:
FedMT: Federated Learning with Mixed-type Labels. CoRR abs/2210.02042 (2022) - [i77]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu
, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i76]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning. CoRR abs/2211.13257 (2022) - [i75]Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li:
Logit Clipping for Robust Learning against Label Noise. CoRR abs/2212.04055 (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]