Gang Niu - RankMe

267 papers A* 92A 5C 4Journal 154Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Are Multimodal Large Language Models Good Annotators for Image Tagging? CoRR Ming-Kun Xie, Jiahao Xiao, Zhiqiang Kou, Zhongnian Li, Gang Niu, Masashi Sugiyama
2026	J	`jnl`	Atom-Motif Contrastive Transformer for Molecular Property Prediction. ACM Trans. Intell. Syst. Technol. Wentao Yu, Shuo Chen, Chen Gong, Bo Han, Gang Niu, Masashi Sugiyama
2026	J	`jnl`	BrokenBind: Universal Modality Exploration beyond Dataset Boundaries. CoRR Zhuo Huang, Runnan Chen, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu
2026	A*	`conf`	Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer. AAAI Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
2025	J	`jnl`	Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms. CoRR Wei Wang, Dong-Dong Wu, Ming Li, Jingxiong Zhang, Gang Niu, Masashi Sugiyama
2025	J	`jnl`	Accurate Forgetting for Heterogeneous Federated Continual Learning. CoRR Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama
2025	A*	`conf`	Adaptive Localization of Knowledge Negation for Continual LLM Unlearning. ICML Abudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2025	J	`jnl`	Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. Wenshui Luo, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong
2025	J	`jnl`	From Coefficients to Directions: Rethinking Model Merging with Directional Alignment. CoRR Zhikang Chen, Sen Cui, Deheng Ye, Min Zhang, Gang Niu, Yu Zhang, Masashi Sugiyama, Tingting Zhu
2025	A*	`conf`	Learning View-invariant World Models for Visual Robotic Manipulation. ICLR Jing-Cheng Pang, Nan Tang, Kaiyuan Li, Yuting Tang, Xin-Qiang Cai, Zhen-Yu Zhang, Gang Niu, Masashi Sugiyama, Yang Yu
2025	A*	`conf`	Learning without Isolation: Pathway Protection for Continual Learning. ICML 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	J	`jnl`	Learning without Isolation: Pathway Protection for Continual Learning. CoRR 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	A*	`conf`	On the Role of Label Noise in the Feature Learning Process. ICML Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki
2025	J	`jnl`	On the Role of Label Noise in the Feature Learning Process. CoRR Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki
2025	A*	`conf`	Realistic Evaluation of Deep Partial-Label Learning Algorithms. ICLR Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2025	J	`jnl`	Realistic Evaluation of Deep Partial-Label Learning Algorithms. CoRR Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2025	J	`jnl`	Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers. CoRR Xin-Qiang Cai, Wei Wang, Feng Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama
2025	J	`jnl`	Rethinking Consistent Multi-Label Classification under Inexact Supervision. CoRR Wei Wang, Tianhao Ma, Ming-Kun Xie, Gang Niu, Masashi Sugiyama
2025	J	`jnl`	Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective. CoRR Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai, Ming-Kun Xie, Biao Liu, Changwei Wang, Lei Feng, Yuheng Jia, Gang Niu, Masashi Sugiyama, Xin Geng
2025	J	`jnl`	Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. CoRR Jie Xu, Na Zhao, Gang Niu, Masashi Sugiyama, Xiaofeng Zhu
2025	A*	`conf`	Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. ICLR Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu
2025	J	`jnl`	Towards Scalable Oversight with Collaborative Multi-Agent Debate in Error Detection. CoRR Yongqiang Chen, Gang Niu, James Cheng, Bo Han, Masashi Sugiyama
2025	J	`jnl`	What Is Preference Optimization Doing, How and Why? CoRR Yue Wang, Qizhou Wang, Zizhuo Zhang, Ang Li, Gang Niu, Bo Han, Masashi Sugiyama
2025	J	`jnl`	What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? CoRR Ming-Kun Xie, Jiahao Xiao, Gang Niu, Lei Feng, Zhiqiang Kou, Min-Ling Zhang, Masashi Sugiyama
2024	A*	`conf`	Accurate Forgetting for Heterogeneous Federated Continual Learning. ICLR Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama
2024	A*	`conf`	Balancing Similarity and Complementarity for Federated Learning. ICML Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2024	J	`jnl`	Balancing Similarity and Complementarity for Federated Learning. CoRR Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2024	A*	`conf`	Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	J	`jnl`	Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	J	`jnl`	Decoupling the Class Label and the Target Concept in Machine Unlearning. CoRR Jianing Zhu, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama
2024	—	`conf`	Direct Distillation Between Different Domains. ECCV (80) Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama
2024	J	`jnl`	Direct Distillation between Different Domains. CoRR Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama
2024	J	`jnl`	Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	—	`conf`	Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. ECCV (52) Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	J	`jnl`	Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. CoRR Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. ICML Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios. CVPR Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang, Gang Niu, Xiaofeng Zhu
2024	J	`jnl`	Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels. CoRR Yuxin Tian, Mouxing Yang, Yuhao Zhou, Jian Wang, Qing Ye, Tongliang Liu, Gang Niu, Jiancheng Lv
2024	J	`jnl`	Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen, Ning Xie, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. ICML Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. ICML Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang
2024	J	`jnl`	Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. CoRR Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang
2024	A*	`conf`	Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. NeurIPS Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang
2024	J	`jnl`	On Unsupervised Prompt Learning for Classification with Black-box Language Models. CoRR Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024	J	`jnl`	On the Effectiveness of Adversarial Training Against Backdoor Attacks. IEEE Trans. Neural Networks Learn. Syst. Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
2024	J	`jnl`	On the Robustness of Average Losses for Partial-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama
2024	J	`jnl`	PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
2024	A*	`conf`	Robust Similarity Learning with Difference Alignment Regularization. ICLR Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama
2024	A*	`conf`	What Makes Partial-Label Learning Algorithms Effective? NeurIPS Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng
2023	J	`jnl`	A Parametrical Model for Instance-Dependent Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. Shuo Yang, Songhua Wu, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu
2023	A*	`conf`	A Universal Unbiased Method for Classification from Aggregate Observations. ICML Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen
2023	J	`jnl`	A Universal Unbiased Method for Classification from Aggregate Observations. CoRR Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen
2023	J	`jnl`	Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Atom-Motif Contrastive Transformer for Molecular Property Prediction. CoRR Wentao Yu, Shuo Chen, Chen Gong, Gang Niu, Masashi Sugiyama
2023	A*	`conf`	Binary Classification with Confidence Difference. NeurIPS Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2023	J	`jnl`	Binary Classification with Confidence Difference. CoRR Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2023	J	`jnl`	Boundary-restricted metric learning. Mach. Learn. Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	A*	`conf`	Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	J	`jnl`	Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. Chen Gong, Yongliang Ding, Bo Han, Gang Niu, Jian Yang, Jane You, Dacheng Tao, Masashi Sugiyama
2023	A*	`conf`	Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. ICCV Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
2023	J	`jnl`	Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
2023	A*	`conf`	Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han
2023	J	`jnl`	Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han
2023	A*	`conf`	Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
2023	J	`jnl`	Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
2023	J	`jnl`	Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2023	J	`jnl`	Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
2023	A*	`conf`	Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. NeurIPS Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios. CoRR Jie Xu, Gang Niu, Xiaolong Wang, Yazhou Ren, Lei Feng, Xiaoshuang Shi, Heng Tao Shen, Xiaofeng Zhu
2023	A*	`conf`	Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. ICLR Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Learning Intention-Aware Policies in Deep Reinforcement Learning. Neural Comput. Tingting Zhao, S. Wu, G. Li, Y. Chen, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu
2023	A*	`conf`	Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. ICML Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li
2023	A*	`conf`	Multi-Label Knowledge Distillation. ICCV Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	J	`jnl`	Multi-Label Knowledge Distillation. CoRR Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	J	`jnl`	Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity. IEEE Trans. Knowl. Data Eng. Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu
2023	J	`jnl`	Representation learning for continuous action spaces is beneficial for efficient policy learning. Neural Networks Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama
2023	A*	`conf`	Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration. NeurIPS Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, Xiaofeng Zhu
2023	A*	`conf`	Towards Effective Visual Representations for Partial-Label Learning. CVPR Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng
2023	J	`jnl`	Towards Effective Visual Representations for Partial-Label Learning. CoRR Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng
2022	A*	`conf`	Adversarial Robustness Through the Lens of Causality. ICLR Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang
2022	A*	`conf`	Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
2022	J	`jnl`	Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
2022	A*	`conf`	Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. ICML Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu
2022	A*	`conf`	Exploiting Class Activation Value for Partial-Label Learning. ICLR Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
2022	A*	`conf`	Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. ICML Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng
2022	J	`jnl`	Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. CoRR Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng
2022	J	`jnl`	Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	FedMT: Federated Learning with Mixed-type Labels. CoRR Qiong Zhang, Aline Talhouk, Gang Niu, Xiaoxiao Li
2022	J	`jnl`	Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
2022	A*	`conf`	Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. ICLR Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
2022	A*	`conf`	Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. NeurIPS Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama
2022	J	`jnl`	Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama
2022	A*	`ed.`	International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. ICML Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato
2022	J	`jnl`	Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Learning Contrastive Embedding in Low-Dimensional Space. NeurIPS Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama
2022	A	`conf`	Learning and Mining with Noisy Labels. CIKM Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu
2022	J	`jnl`	Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. ICLR Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu
2022	J	`jnl`	Logit Clipping for Robust Learning against Label Noise. CoRR Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li
2022	A*	`conf`	Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
2022	J	`jnl`	NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	On the Effectiveness of Adversarial Training against Backdoor Attacks. CoRR Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	PiCO: Contrastive Label Disambiguation for Partial Label Learning. ICLR Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
2022	J	`jnl`	PiCO: Contrastive Label Disambiguation for Partial Label Learning. CoRR Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
2022	A*	`conf`	Reliable Adversarial Distillation with Unreliable Teachers. ICLR Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang
2022	J	`jnl`	Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning. CoRR Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
2022	A*	`conf`	Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. Trans. Mach. Learn. Res. Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long
2022	A*	`conf`	To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
2021	J	`jnl`	Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2021	J	`jnl`	Adversarial Robustness through the Lens of Causality. CoRR Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang
2021	A*	`conf`	Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama
2021	J	`jnl`	Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama
2021	A*	`conf`	CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. ICML Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama
2021	J	`jnl`	CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. CoRR Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama
2021	A*	`conf`	Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. ICML Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu
2021	A*	`conf`	Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
2021	J	`jnl`	Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2021	J	`jnl`	Estimating Instance-dependent Label-noise Transition Matrix using DNNs. CoRR Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu
2021	A*	`conf`	Geometry-aware Instance-reweighted Adversarial Training. ICLR Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli
2021	J	`jnl`	Guided Interpolation for Adversarial Training. CoRR Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama
2021	J	`jnl`	Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Instance Correction for Learning with Open-set Noisy Labels. CoRR Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
2021	J	`jnl`	Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
2021	A*	`conf`	Large-Margin Contrastive Learning with Distance Polarization Regularizer. ICML Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama
2021	A*	`conf`	Learning Diverse-Structured Networks for Adversarial Robustness. ICML Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama
2021	J	`jnl`	Learning Diverse-Structured Networks for Adversarial Robustness. CoRR Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama
2021	A*	`conf`	Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML Yivan Zhang, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR Yivan Zhang, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Learning from Similarity-Confidence Data. ICML Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Learning from Similarity-Confidence Data. CoRR Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. CoRR Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu
2021	J	`jnl`	Local Reweighting for Adversarial Training. CoRR Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng
2021	A*	`conf`	Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han
2021	J	`jnl`	Multi-Class Classification from Single-Class Data with Confidences. CoRR Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Multiple-Instance Learning from Similar and Dissimilar Bags. KDD Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu
2021	J	`jnl`	NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama
2021	J	`jnl`	On the Robustness of Average Losses for Partial-Label Learning. CoRR Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama
2021	A*	`conf`	Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
2021	A*	`conf`	Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
2021	J	`jnl`	Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
2021	J	`jnl`	Provably End-to-end Label-Noise Learning without Anchor Points. CoRR Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Provably End-to-end Label-noise Learning without Anchor Points. ICML Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Reliable Adversarial Distillation with Unreliable Teachers. CoRR Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang
2021	J	`jnl`	Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
2021	A	`conf`	Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama
2021	A*	`conf`	Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong
2021	J	`jnl`	Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong
2021	J	`jnl`	Understanding (Generalized) Label Smoothing when Learning with Noisy Labels. CoRR Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu
2021	A*	`conf`	Understanding and Improving Early Stopping for Learning with Noisy Labels. NeurIPS Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu
2021	J	`jnl`	Understanding and Improving Early Stopping for Learning with Noisy Labels. CoRR Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu
2021	J	`jnl`	Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama
2020	J	`jnl`	A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama
2020	A*	`conf`	Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. ICML Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli
2020	J	`jnl`	Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CoRR Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli
2020	A*	`conf`	Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. AAAI Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao
2020	J	`jnl`	Class2Simi: A New Perspective on Learning with Label Noise. CoRR Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu
2020	J	`jnl`	Confidence Scores Make Instance-dependent Label-noise Learning Possible. CoRR Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
2020	A*	`conf`	Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. ICDM Chun Wang, Bo Han, Shirui Pan, Jing Jiang, Gang Niu, Guodong Long
2020	A*	`conf`	Do We Need Zero Training Loss After Achieving Zero Training Error? ICML Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	A*	`conf`	Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. NeurIPS Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. CoRR Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Geometry-aware Instance-reweighted Adversarial Training. CoRR Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli
2020	A*	`conf`	Learning with Multiple Complementary Labels. ICML Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
2020	J	`jnl`	Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
2020	A	`conf`	Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. AISTATS Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Multi-Class Classification from Noisy-Similarity-Labeled Data. CoRR Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu
2020	A*	`conf`	Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
2020	J	`jnl`	Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
2020	J	`jnl`	Pointwise Binary Classification with Pairwise Confidence Comparisons. CoRR Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
2020	A*	`conf`	Progressive Identification of True Labels for Partial-Label Learning. ICML Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama
2020	J	`jnl`	Progressive Identification of True Labels for Partial-Label Learning. CoRR Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama
2020	A*	`conf`	Provably Consistent Partial-Label Learning. NeurIPS Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
2020	J	`jnl`	Provably Consistent Partial-Label Learning. CoRR Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
2020	A*	`conf`	Rethinking Importance Weighting for Deep Learning under Distribution Shift. NeurIPS Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Rethinking Importance Weighting for Deep Learning under Distribution Shift. CoRR Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2020	A*	`conf`	SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama
2020	A*	`conf`	Searching to Exploit Memorization Effect in Learning with Noisy Labels. ICML Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok
2020	J	`jnl`	SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. CoRR Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long
2020	J	`jnl`	Towards Mixture Proportion Estimation without Irreducibility. CoRR Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
2020	A*	`conf`	Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. ICML Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2020	J	`jnl`	Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. CoRR Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2019	A*	`conf`	Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama
2019	A*	`conf`	Classification from Positive, Unlabeled and Biased Negative Data. ICML Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
2019	A*	`conf`	Complementary-Label Learning for Arbitrary Losses and Models. ICML Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2019	J	`jnl`	Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2019	J	`jnl`	Fast and Robust Rank Aggregation against Model Misspecification. CoRR Yuangang Pan, Weijie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
2019	A*	`conf`	How does Disagreement Help Generalization against Label Corruption? ICML Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
2019	J	`jnl`	How does Disagreement Help Generalization against Label Corruption? CoRR Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
2019	J	`jnl`	Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. CoRR Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama
2019	—	`conf`	On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. ICLR (Poster) Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2019	J	`jnl`	Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. CoRR Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama
2019	J	`jnl`	Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. CoRR Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama
2019	J	`jnl`	Searching to Exploit Memorization Effect in Learning from Corrupted Labels. CoRR Hansi Yang, Quanming Yao, Bo Han, Gang Niu
2019	A*	`conf`	Uncoupled Regression from Pairwise Comparison Data. NeurIPS Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Uncoupled Regression from Pairwise Comparison Data. CoRR Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Where is the Bottleneck of Adversarial Learning with Unlabeled Data? CoRR Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
2018	A*	`conf`	Active Feature Acquisition with Supervised Matrix Completion. KDD Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen
2018	J	`jnl`	Active Feature Acquisition with Supervised Matrix Completion. CoRR Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen
2018	J	`jnl`	Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data. CoRR Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama
2018	A*	`conf`	Binary Classification from Positive-Confidence Data. NeurIPS Takashi Ishida, Gang Niu, Masashi Sugiyama
2018	A*	`conf`	Classification from Pairwise Similarity and Unlabeled Data. ICML Han Bao, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Classification from Pairwise Similarity and Unlabeled Data. CoRR Han Bao, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Classification from Positive, Unlabeled and Biased Negative Data. CoRR Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Co-sampling: Training Robust Networks for Extremely Noisy Supervision. CoRR Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama
2018	A*	`conf`	Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama
2018	J	`jnl`	Complementary-Label Learning for Arbitrary Losses and Models. CoRR Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2018	J	`jnl`	Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2018	A*	`conf`	Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama
2018	A*	`conf`	Masking: A New Perspective of Noisy Supervision. NeurIPS Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama
2018	J	`jnl`	Masking: A New Perspective of Noisy Supervision. CoRR Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama
2018	J	`jnl`	Matrix Co-completion for Multi-label Classification with Missing Features and Labels. CoRR Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama
2018	J	`jnl`	On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. CoRR Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2018	J	`jnl`	Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. CoRR Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama
2018	J	`jnl`	Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. Neural Comput. Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Binary Classification from Positive-Confidence Data. CoRR Takashi Ishida, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Class-prior estimation for learning from positive and unlabeled data. Mach. Learn. Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. CoRR Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	—	`conf`	Learning from Complementary Labels. NIPS Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama
2017	J	`jnl`	Learning from Complementary Labels. CoRR Takashi Ishida, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
2017	—	`conf`	Positive-Unlabeled Learning with Non-Negative Risk Estimator. NIPS Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama
2017	J	`jnl`	Positive-Unlabeled Learning with Non-Negative Risk Estimator. CoRR Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama
2017	J	`jnl`	Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning. CoRR Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	A*	`conf`	Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2017	C	`conf`	Whitening-Free Least-Squares Non-Gaussian Component Analysis. ACML Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. CoRR Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Class-prior Estimation for Learning from Positive and Unlabeled Data. CoRR Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Direct Density Derivative Estimation. Neural Comput. Hiroaki Sasaki, Yung-Kyun Noh, Gang Niu, Masashi Sugiyama
2016	A	`conf`	Non-Gaussian Component Analysis with Log-Density Gradient Estimation. AISTATS Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. CoRR Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama
2016	—	`conf`	Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. NIPS Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
2015	C	`conf`	Class-prior Estimation for Learning from Positive and Unlabeled Data. ACML Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2015	A*	`conf`	Convex Formulation for Learning from Positive and Unlabeled Data. ICML Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2015	C	`conf`	Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. ACML Tingting Zhao, Gang Niu, Ning Xie, JuCheng Yang, Masashi Sugiyama
2014	—	`conf`	Analysis of Learning from Positive and Unlabeled Data. NIPS Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2014	J	`jnl`	Information-Maximization Clustering Based on Squared-Loss Mutual Information. Neural Comput. Masashi Sugiyama, Gang Niu, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya
2014	J	`jnl`	Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization. Neural Comput. Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
2014	J	`jnl`	Semi-supervised information-maximization clustering. Neural Networks Daniele Calandriello, Gang Niu, Masashi Sugiyama
2014	A*	`conf`	Transductive Learning with Multi-class Volume Approximation. ICML Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama
2014	J	`jnl`	Transductive Learning with Multi-class Volume Approximation. CoRR Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama
2013	—	`conf`	Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances. TAAI Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2013	J	`jnl`	Maximum volume clustering: a new discriminative clustering approach. J. Mach. Learn. Res. Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama
2013	J	`jnl`	Semi-Supervised Information-Maximization Clustering CoRR Daniele Calandriello, Gang Niu, Masashi Sugiyama
2013	—	`conf`	Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. ICML (3) Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama
2012	J	`jnl`	Analysis and improvement of policy gradient estimation. Neural Networks Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
2012	J	`jnl`	Information-theoretic Semi-supervised Metric Learning via Entropy Regularization CoRR Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
2012	A*	`conf`	Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. ICML Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
2011	—	`conf`	Analysis and Improvement of Policy Gradient Estimation. NIPS Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
2011	A	`conf`	Maximum Volume Clustering. AISTATS Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama
2011	C	`conf`	Suffcient Component Analysis. ACML Makoto Yamada, Gang Niu, Jun Takagi, Masashi Sugiyama
2011	J	`jnl`	Transfer Learning via Multi-View Principal Component Analysis. J. Comput. Sci. Technol. Yangsheng Ji, Jiajun Chen, Gang Niu, Lin Shang, Xinyu Dai
2010	A*	`conf`	Bayesian Maximum Margin Clustering. ICDM Bo Dai, Bao-Gang Hu, Gang Niu
2010	—	`conf`	Compact Margin Machine. PAKDD (2) Bo Dai, Gang Niu
2010	—	`conf`	Rough Margin Based Core Vector Machine. PAKDD (1) Gang Niu, Bo Dai, Lin Shang, Yangsheng Ji