Gang Niu

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 198

Top Venues

Publications

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 (2024)
Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama
Direct Distillation between Different Domains. CoRR (2024)
Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR (2023)
Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR (2023)
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 (2023)
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 (2023)
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
Multi-Label Knowledge Distillation. CoRR (2023)
Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR (2023)
Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR (2023)
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)
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)
Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng
Towards Effective Visual Representations for Partial-Label Learning. CVPR (2023)
Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
Binary Classification with Confidence Difference. NeurIPS (2023)
Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. ICCV (2023)
Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR (2023)
Ming-Kun Xie, Jiahao Xiao, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR (2023)
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) (2023)
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 (2023)
Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng
Towards Effective Visual Representations for Partial-Label Learning. CoRR (2023)
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)
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 (2023)
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)
Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
Binary Classification with Confidence Difference. CoRR (2023)
Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama
Boundary-restricted metric learning. Mach. Learn. 112 (12) (2023)
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) (2023)
Tingting Zhao, S. Wu, G. Li, Y. Chen, Gang Niu, Masashi Sugiyama
Learning Intention-Aware Policies in Deep Reinforcement Learning. Neural Comput. 35 (10) (2023)
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)
Wentao Yu, Shuo Chen, Chen Gong, Gang Niu, Masashi Sugiyama
Atom-Motif Contrastive Transformer for Molecular Property Prediction. CoRR (2023)
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 (2023)
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) (2023)
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)
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)
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
Multi-Label Knowledge Distillation. ICCV (2023)
Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR (2023)
Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. NeurIPS (2023)
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)
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)
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)
Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama
Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. 23 (2022)
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR (2022)
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 (2022)
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)
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)
Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR (2022)
Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
Exploiting Class Activation Value for Partial-Label Learning. ICLR (2022)
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 (2022)
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)
Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
PiCO: Contrastive Label Disambiguation for Partial Label Learning. CoRR (2022)
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)
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)
Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li
Logit Clipping for Robust Learning against Label Noise. CoRR (2022)
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)
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)
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)
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML (2022)
Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu
Learning and Mining with Noisy Labels. CIKM (2022)
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 (2022)
Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
PiCO: Contrastive Label Disambiguation for Partial Label Learning. ICLR (2022)
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 (2022)
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)
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 (2022)
Qiong Zhang, Aline Talhouk, Gang Niu, Xiaoxiao Li
FedMT: Federated Learning with Mixed-type Labels. CoRR (2022)
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 (2022)
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)
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 (2022)
Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama
Learning Contrastive Embedding in Low-Dimensional Space. NeurIPS (2022)
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang
Adversarial Robustness through the Lens of Causality. CoRR (2021)
Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
Multi-Class Classification from Single-Class Data with Confidences. CoRR (2021)
Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu
Estimating Instance-dependent Label-noise Transition Matrix using DNNs. CoRR (2021)
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 (2021)
Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS (2021)
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) (2021)
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 (2021)
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 (2021)
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)
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)
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)
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 (2021)
Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI (2021)
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)
Yivan Zhang, Gang Niu, Masashi Sugiyama
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR (2021)
Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng
Local Reweighting for Adversarial Training. CoRR (2021)
Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
Learning from Similarity-Confidence Data. ICML (2021)
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)
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 (2021)
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 (2021)
Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR (2021)
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33 (1) (2021)
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
Instance Correction for Learning with Open-set Noisy Labels. CoRR (2021)
Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR (2021)
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 (2021)
Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR (2021)
Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama
Guided Interpolation for Adversarial Training. CoRR (2021)
Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR (2021)
Yivan Zhang, Gang Niu, Masashi Sugiyama
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML (2021)
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML (2021)
Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR (2021)
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 (2021)
Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR (2021)
Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Gang Niu