Gang Niu
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
83
Venues
13
Active years
2010–2026
Best venue rank
A*
Where they publish
Papers
83 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer. | Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu |
| 2025 | ICCV | Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. | Jie Xu, Na Zhao, Gang Niu, Masashi Sugiyama, Xiaofeng Zhu |
| 2025 | ICLR | Realistic Evaluation of Deep Partial-Label Learning Algorithms. | Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama |
| 2025 | ICLR | Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. | Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu |
| 2025 | ICLR | Learning View-invariant World Models for Visual Robotic Manipulation. | Jing-Cheng Pang, Nan Tang, Kaiyuan Li, Yuting Tang, Xin-Qiang Cai, Zhen-Yu Zhang, Gang Niu, Masashi Sugiyama, Yang Yu |
| 2025 | ICML | Learning without Isolation: Pathway Protection for Continual Learning. | Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui, Haoxuan Li, Ding Li, Jingfeng Zhang, Bo Han, Gang Niu, Houfang Liu, Yi Yang, Sifan Yang, Changshui Zhang, Tianling Ren |
| 2025 | ICML | On the Role of Label Noise in the Feature Learning Process. | Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki |
| 2025 | ICML | Adaptive Localization of Knowledge Negation for Continual LLM Unlearning. | Abudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang |
| 2024 | CVPR | Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios. | Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang, Gang Niu, Xiaofeng Zhu |
| 2024 | ECCV | Direct Distillation Between Different Domains. | Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama |
| 2024 | ECCV | Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. | Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2024 | ICLR | Robust Similarity Learning with Difference Alignment Regularization. | Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama |
| 2024 | ICLR | Accurate Forgetting for Heterogeneous Federated Continual Learning. | Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama |
| 2024 | ICML | Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. | Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang |
| 2024 | ICML | Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. | Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama |
| 2024 | ICML | Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. | Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2024 | ICML | Balancing Similarity and Complementarity for Federated Learning. | Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang |
| 2024 | ICML | Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. | Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama |
| 2023 | CVPR | Towards Effective Visual Representations for Partial-Label Learning. | Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng |
| 2023 | ICCV | Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. | Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong |
| 2023 | ICCV | Multi-Label Knowledge Distillation. | Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2023 | ICLR | Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. | Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama |
| 2023 | ICML | Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. | Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han |
| 2023 | ICML | A Universal Unbiased Method for Classification from Aggregate Observations. | Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen |
| 2023 | ICML | Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. | Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li |
| 2022 | CIKM | Learning and Mining with Noisy Labels. | Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu |
| 2022 | CVPR | Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. | De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama |
| 2022 | ICLR | Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. | Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama |
| 2022 | ICLR | Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. | Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama |
| 2022 | ICLR | PiCO: Contrastive Label Disambiguation for Partial Label Learning. | Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao |
| 2022 | ICLR | Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. | Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu |
| 2022 | ICLR | Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. | Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama |
| 2022 | ICLR | Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. | Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao |
| 2022 | ICLR | Exploiting Class Activation Value for Partial-Label Learning. | Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama |
| 2022 | ICLR | Adversarial Robustness Through the Lens of Causality. | Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schlkopf, Kun Zhang |
| 2022 | ICLR | Reliable Adversarial Distillation with Unreliable Teachers. | Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang |
| 2022 | ICML | Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. | Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng |
| 2022 | ICML | To Smooth or Not? When Label Smoothing Meets Noisy Labels. | Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu |
| 2022 | ICML | Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. | Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu |
| 2021 | AAAI | Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. | Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong |
| 2021 | EACL | Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. | Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama |
| 2021 | ICLR | Geometry-aware Instance-reweighted Adversarial Training. | Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli |
| 2021 | ICML | Large-Margin Contrastive Learning with Distance Polarization Regularizer. | Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama |
| 2021 | ICML | Confidence Scores Make Instance-dependent Label-noise Learning Possible. | Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama |
| 2021 | ICML | Learning from Similarity-Confidence Data. | Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama |
| 2021 | ICML | Learning Diverse-Structured Networks for Adversarial Robustness. | Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama |
| 2021 | ICML | Pointwise Binary Classification with Pairwise Confidence Comparisons. | Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama |
| 2021 | ICML | Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. | Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama |
| 2021 | ICML | Provably End-to-end Label-noise Learning without Anchor Points. | Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama |
| 2021 | ICML | Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. | Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama |
| 2021 | ICML | Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. | Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu |
| 2021 | ICML | CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. | Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama |
| 2021 | ICML | Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. | Yivan Zhang, Gang Niu, Masashi Sugiyama |
| 2021 | KDD | Multiple-Instance Learning from Similar and Dissimilar Bags. | Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu |
| 2020 | AAAI | Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. | Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao |
| 2020 | AISTATS | Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. | Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama |
| 2020 | ICDM | Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. | Chun Wang, Bo Han, Shirui Pan, Jing Jiang, Gang Niu, Guodong Long |
| 2020 | ICML | SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. | Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama |
| 2020 | ICML | Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. | Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama |
| 2020 | ICML | Learning with Multiple Complementary Labels. | Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama |
| 2020 | ICML | Do We Need Zero Training Loss After Achieving Zero Training Error? | Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama |
| 2020 | ICML | Progressive Identification of True Labels for Partial-Label Learning. | Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama |
| 2020 | ICML | Searching to Exploit Memorization Effect in Learning with Noisy Labels. | Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok |
| 2020 | ICML | Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. | Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli |
| 2019 | ICLR | On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. | Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama |
| 2019 | ICML | Classification from Positive, Unlabeled and Biased Negative Data. | Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama |
| 2019 | ICML | Complementary-Label Learning for Arbitrary Losses and Models. | Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama |
| 2019 | ICML | How does Disagreement Help Generalization against Label Corruption? | Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama |
| 2018 | ICML | Classification from Pairwise Similarity and Unlabeled Data. | Han Bao, Gang Niu, Masashi Sugiyama |
| 2018 | ICML | Does Distributionally Robust Supervised Learning Give Robust Classifiers? | Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama |
| 2018 | KDD | Active Feature Acquisition with Supervised Matrix Completion. | Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen |
| 2017 | ACML | Whitening-Free Least-Squares Non-Gaussian Component Analysis. | Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama |
| 2017 | ICML | Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. | Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2016 | AISTATS | Non-Gaussian Component Analysis with Log-Density Gradient Estimation. | Hiroaki Sasaki, Gang Niu, Masashi Sugiyama |
| 2015 | ACML | Class-prior Estimation for Learning from Positive and Unlabeled Data. | Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2015 | ACML | Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. | Tingting Zhao, Gang Niu, Ning Xie, Jucheng Yang, Masashi Sugiyama |
| 2015 | ICML | Convex Formulation for Learning from Positive and Unlabeled Data. | Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2014 | ICML | Transductive Learning with Multi-class Volume Approximation. | Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama |
| 2013 | ICML | Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. | Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama |
| 2012 | ICML | Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. | Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama |
| 2010 | ICDM | Bayesian Maximum Margin Clustering. | Bo Dai, Bao-Gang Hu, Gang Niu |
| 2010 | PAKDD | Compact Margin Machine. | Bo Dai, Gang Niu |
| 2010 | PAKDD | Rough Margin Based Core Vector Machine. | Gang Niu, Bo Dai, Lin Shang, Yangsheng Ji |