Niu, Gang

114 publications

ICML 2025 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

ICLR 2025 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

ICML 2025 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

ICML 2025 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

ICLR 2025 Realistic Evaluation of Deep Partial-Label Learning Algorithms Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama

ICCV 2025 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

ICLR 2025 Towards Out-of-Modal Generalization Without Instance-Level Modal Correspondence Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu

ICLR 2024 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

ICML 2024 Balancing Similarity and Complementarity for Federated Learning Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang

ICML 2024 Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training Ming-Kun Xie, Jia-Hao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang

ECCV 2024 Direct Distillation Between Different Domains Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama

ECCV 2024 Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning Jia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang

ICML 2024 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

CVPR 2024 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

ICML 2024 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

ICML 2024 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

NeurIPS 2024 Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang

ICLR 2024 Robust Similarity Learning with Difference Alignment Regularization Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama

NeurIPS 2024 What Makes Partial-Label Learning Algorithms Effective? Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng

ICML 2023 A Universal Unbiased Method for Classification from Aggregate Observations Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen

NeurIPS 2023 Binary Classification with Confidence Difference Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama

MLJ 2023 Boundary-Restricted Metric Learning Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama

NeurIPS 2023 Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang

ICCV 2023 Distribution Shift Matters for Knowledge Distillation with Webly Collected Images Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong

NeurIPS 2023 Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han

ICML 2023 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

NeurIPS 2023 Generalizing Importance Weighting to a Universal Solver for Distribution Shift Problems Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama

ICLR 2023 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

ICML 2023 Mitigating Memorization of Noisy Labels by Clipping the Model Prediction Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li

ICCV 2023 Multi-Label Knowledge Distillation Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang

NeurIPS 2023 Self-Weighted Contrastive Learning Among Multiple Views for Mitigating Representation Degeneration Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, Xiaofeng Zhu

CVPR 2023 Towards Effective Visual Representations for Partial-Label Learning Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng

ICLR 2022 Adversarial Robustness Through the Lens of Causality Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

NeurIPS 2022 Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama

ICML 2022 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

ICLR 2022 Exploiting Class Activation Value for Partial-Label Learning Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama

ICML 2022 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

JMLR 2022 Fast and Robust Rank Aggregation Against Model Misspecification Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama

ICLR 2022 Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

NeurIPS 2022 Generalizing Consistent Multi-Class Classification with Rejection to Be Compatible with Arbitrary Losses Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama

CVPR 2022 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

NeurIPS 2022 Learning Contrastive Embedding in Low-Dimensional Space Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama

JMLR 2022 Learning from Noisy Pairwise Similarity and Unlabeled Data Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama

ICLR 2022 Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu

ICLR 2022 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

TMLR 2022 NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama

ICLR 2022 PiCO: Contrastive Label Disambiguation for Partial Label Learning Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao

ICLR 2022 Reliable Adversarial Distillation with Unreliable Teachers Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang

ICLR 2022 Rethinking Class-Prior Estimation for Positive-Unlabeled Learning Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao

ICLR 2022 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

TMLR 2022 SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long

ICML 2022 To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu

ICML 2021 Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama

ICML 2021 CIFS: Improving Adversarial Robustness of CNNs via Channel-Wise Importance-Based Feature Selection Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Tan, Masashi Sugiyama

ICML 2021 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

ICML 2021 Confidence Scores Make Instance-Dependent Label-Noise Learning Possible Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama

ICLR 2021 Geometry-Aware Instance-Reweighted Adversarial Training Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan Kankanhalli

NeurIPS 2021 Instance-Dependent Label-Noise Learning Under a Structural Causal Model Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang

ICML 2021 Large-Margin Contrastive Learning with Distance Polarization Regularizer Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama

ICML 2021 Learning Diverse-Structured Networks for Adversarial Robustness Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama

ICML 2021 Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization Yivan Zhang, Gang Niu, Masashi Sugiyama

ICML 2021 Learning from Similarity-Confidence Data Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama

ICML 2021 Maximum Mean Discrepancy Test Is Aware of Adversarial Attacks Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

NeurIPSW 2021 On the Role of Pre-Training for Meta Few-Shot Learning Chia-You Chen, Hsuan-Tien Lin, Masashi Sugiyama, Gang Niu

ICML 2021 Pointwise Binary Classification with Pairwise Confidence Comparisons Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama

NeurIPS 2021 Probabilistic Margins for Instance Reweighting in Adversarial Training Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

ICML 2021 Provably End-to-End Label-Noise Learning Without Anchor Points Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama

AAAI 2021 Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong

NeurIPS 2021 Understanding and Improving Early Stopping for Learning with Noisy Labels Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu

ICML 2020 Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli

AAAI 2020 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

ICML 2020 Do We Need Zero Training Loss After Achieving Zero Training Error? Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama

NeurIPS 2020 Dual T: Reducing Estimation Error for Transition Matrix in Label-Noise Learning Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama

ICML 2020 Learning with Multiple Complementary Labels Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama

AISTATS 2020 Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama

NeurIPS 2020 Part-Dependent Label Noise: Towards Instance-Dependent Label Noise Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama

ICML 2020 Progressive Identification of True Labels for Partial-Label Learning Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama

NeurIPS 2020 Provably Consistent Partial-Label Learning Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama

NeurIPS 2020 Rethinking Importance Weighting for Deep Learning Under Distribution Shift Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama

ICML 2020 SIGUA: Forgetting May Make Learning with Noisy Labels More Robust Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama

ICML 2020 Searching to Exploit Memorization Effect in Learning with Noisy Labels Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok

ICML 2020 Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

ICLRW 2019 A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision Cheng-Yu Hsieh, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

NeurIPS 2019 Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama

ICML 2019 Classification from Positive, Unlabeled and Biased Negative Data Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama

ICML 2019 Complementary-Label Learning for Arbitrary Losses and Models Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama

ICML 2019 How Does Disagreement Help Generalization Against Label Corruption? Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, Masashi Sugiyama

ICLR 2019 On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama

NeurIPS 2019 Uncoupled Regression from Pairwise Comparison Data Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama

NeurIPS 2018 Binary Classification from Positive-Confidence Data Takashi Ishida, Gang Niu, Masashi Sugiyama

ICML 2018 Classification from Pairwise Similarity and Unlabeled Data Han Bao, Gang Niu, Masashi Sugiyama

NeurIPS 2018 Co-Teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama

MLJ 2018 Correction to: Semi-Supervised AUC Optimization Based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu, Masashi Sugiyama

ICML 2018 Does Distributionally Robust Supervised Learning Give Robust Classifiers? Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama

NeurIPS 2018 Masking: A New Perspective of Noisy Supervision Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, Masashi Sugiyama

MLJ 2018 Semi-Supervised AUC Optimization Based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu, Masashi Sugiyama

MLJ 2017 Class-Prior Estimation for Learning from Positive and Unlabeled Data Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama

NeurIPS 2017 Learning from Complementary Labels Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama

NeurIPS 2017 Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo, Gang Niu, Marthinus C du Plessis, Masashi Sugiyama

ICML 2017 Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama

ACML 2017 Whitening-Free Least-Squares Non-Gaussian Component Analysis Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama

AISTATS 2016 Non-Gaussian Component Analysis with Log-Density Gradient Estimation Hiroaki Sasaki, Gang Niu, Masashi Sugiyama

NeurIPS 2016 Theoretical Comparisons of Positive-Unlabeled Learning Against Positive-Negative Learning Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama

ACML 2015 Class-Prior Estimation for Learning from Positive and Unlabeled Data Marthinus Christoffel, Gang Niu, Masashi Sugiyama

ICML 2015 Convex Formulation for Learning from Positive and Unlabeled Data Marthinus Du Plessis, Gang Niu, Masashi Sugiyama

ACML 2015 Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation Tingting Zhao, Gang Niu, Ning Xie, Jucheng Yang, Masashi Sugiyama

NeurIPS 2014 Analysis of Learning from Positive and Unlabeled Data Marthinus C du Plessis, Gang Niu, Masashi Sugiyama

ICML 2014 Transductive Learning with Multi-Class Volume Approximation Gang Niu, Bo Dai, Christoffel Plessis, Masashi Sugiyama

JMLR 2013 Maximum Volume Clustering: A New Discriminative Clustering Approach Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama

ICML 2013 Squared-Loss Mutual Information Regularization: A Novel Information-Theoretic Approach to Semi-Supervised Learning Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama

ICML 2012 Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama

NeurIPS 2011 Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama

ACML 2011 Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information Makoto Yamada, Gang Niu, Jun Takagi, Masashi Sugiyama

AISTATS 2011 Maximum Volume Clustering Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama