Bayesian Maximum Margin Clustering. - Bo Dai 001; Bao-Gang Hu; Gang Niu 001 (ICDM, 2010)

Compact Margin Machine. - Bo Dai 001; Gang Niu 001 (PAKDD (2), 2010)

Rough Margin Based Core Vector Machine. - Gang Niu 001; Bo Dai 001; Lin Shang 001; Yangsheng Ji (PAKDD (1), 2010)

Transfer Learning via Multi-View Principal Component Analysis. - Yangsheng Ji; Jiajun Chen 001; Gang Niu 001; Lin Shang 001; Xinyu Dai (J. Comput. Sci. Technol., 2011)

Maximum Volume Clustering. - Gang Niu 001; Bo Dai 001; Lin Shang 001; Masashi Sugiyama (AISTATS, 2011)

Suffcient Component Analysis. - Makoto Yamada; Gang Niu 001; Jun Takagi; Masashi Sugiyama (ACML, 2011)

Analysis and improvement of policy gradient estimation. - Tingting Zhao 001; Hirotaka Hachiya; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2012)

Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. - Gang Niu 001; Wittawat Jitkrittum; Bo Dai 001; Hirotaka Hachiya; Masashi Sugiyama (ICML (3), 2013)

Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (TAAI, 2013)

Maximum volume clustering: a new discriminative clustering approach. - Gang Niu 001; Bo Dai 001; Lin Shang 001; Masashi Sugiyama (J. Mach. Learn. Res., 2013)

Transductive Learning with Multi-class Volume Approximation. - Gang Niu 001; Bo Dai 001; Marthinus Christoffel du Plessis; Masashi Sugiyama (ICML, 2014)

Analysis of Learning from Positive and Unlabeled Data. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (NIPS, 2014)

Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization. - Gang Niu 001; Bo Dai 001; Makoto Yamada; Masashi Sugiyama (Neural Comput., 2014)

Information-Maximization Clustering Based on Squared-Loss Mutual Information. - Masashi Sugiyama; Gang Niu 001; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya (Neural Comput., 2014)

Semi-supervised information-maximization clustering. - Daniele Calandriello; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2014)

Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. - Tingting Zhao 001; Gang Niu 001; Ning Xie 003; Jucheng Yang 001; Masashi Sugiyama (ACML, 2015)

Convex Formulation for Learning from Positive and Unlabeled Data. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (ICML, 2015)

Non-Gaussian Component Analysis with Log-Density Gradient Estimation. - Hiroaki Sasaki; Gang Niu 001; Masashi Sugiyama (AISTATS, 2016)

Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. - Gang Niu 001; Marthinus Christoffel du Plessis; Tomoya Sakai 001; Yao Ma; Masashi Sugiyama (NIPS, 2016)

Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. - Gang Niu 001; Marthinus Christoffel du Plessis; Tomoya Sakai 001; Masashi Sugiyama (CoRR, 2016)

Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. - Tomoya Sakai 001; Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (CoRR, 2016)

Direct Density Derivative Estimation. - Hiroaki Sasaki; Yung-Kyun Noh; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2016)

Whitening-Free Least-Squares Non-Gaussian Component Analysis. - Hiroaki Shiino; Hiroaki Sasaki; Gang Niu 001; Masashi Sugiyama (ACML, 2017)

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. - Tomoya Sakai 001; Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (ICML, 2017)

Learning from Complementary Labels. - Takashi Ishida 001; Gang Niu 001; Weihua Hu; Masashi Sugiyama (NIPS, 2017)

Positive-Unlabeled Learning with Non-Negative Risk Estimator. - Ryuichi Kiryo; Gang Niu 001; Marthinus Christoffel du Plessis; Masashi Sugiyama (NIPS, 2017)

Learning from Complementary Labels. - Takashi Ishida 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2017)

Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2017)

Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. - Hiroaki Sasaki; Takafumi Kanamori; Aapo Hyvärinen; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2017)

Class-prior estimation for learning from positive and unlabeled data. - Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2017)

Classification from Pairwise Similarity and Unlabeled Data. - Han Bao 002; Gang Niu 001; Masashi Sugiyama (ICML, 2018)

Does Distributionally Robust Supervised Learning Give Robust Classifiers? - Weihua Hu; Gang Niu 001; Issei Sato; Masashi Sugiyama (ICML, 2018)

Active Feature Acquisition with Supervised Matrix Completion. - Sheng-Jun Huang; Miao Xu 001; Ming-Kun Xie; Masashi Sugiyama; Gang Niu 001; Songcan Chen (KDD, 2018)

Masking: A New Perspective of Noisy Supervision. - Bo Han 003; Jiangchao Yao; Gang Niu 001; Mingyuan Zhou; Ivor W. Tsang; Ya Zhang 002; Masashi Sugiyama (NeurIPS, 2018)

Co-teaching: Robust training of deep neural networks with extremely noisy labels. - Bo Han 003; Quanming Yao; Xingrui Yu; Gang Niu 001; Miao Xu 001; Weihua Hu; Ivor W. Tsang; Masashi Sugiyama (NeurIPS, 2018)

Binary Classification from Positive-Confidence Data. - Takashi Ishida 001; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2018)

Co-sampling: Training Robust Networks for Extremely Noisy Supervision. - Bo Han 003; Quanming Yao; Xingrui Yu; Gang Niu 001; Miao Xu 001; Weihua Hu; Ivor W. Tsang; Masashi Sugiyama (CoRR, 2018)

Matrix Co-completion for Multi-label Classification with Missing Features and Labels. - Miao Xu 001; Gang Niu 001; Bo Han 003; Ivor W. Tsang; Zhi-Hua Zhou; Masashi Sugiyama (CoRR, 2018)

Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data. - Masahiro Kato; Liyuan Xu; Gang Niu 001; Masashi Sugiyama (CoRR, 2018)

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. - Bo Han 003; Gang Niu 001; Jiangchao Yao; Xingrui Yu; Miao Xu 001; Ivor W. Tsang; Masashi Sugiyama (CoRR, 2018)

Semi-supervised AUC optimization based on positive-unlabeled learning. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2018)

Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2018)

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. - Hiroaki Sasaki; Voot Tangkaratt; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2018)

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. - Nan Lu; Gang Niu 001; Aditya Krishna Menon; Masashi Sugiyama (ICLR (Poster), 2019)

Classification from Positive, Unlabeled and Biased Negative Data. - Yu-Guan Hsieh; Gang Niu 001; Masashi Sugiyama (ICML, 2019)

Complementary-Label Learning for Arbitrary Losses and Models. - Takashi Ishida 001; Gang Niu 001; Aditya Krishna Menon; Masashi Sugiyama (ICML, 2019)

How does Disagreement Help Generalization against Label Corruption? - Xingrui Yu; Bo Han 003; Jiangchao Yao; Gang Niu 001; Ivor W. Tsang; Masashi Sugiyama (ICML, 2019)

Are Anchor Points Really Indispensable in Label-Noise Learning? - Xiaobo Xia; Tongliang Liu; Nannan Wang 001; Bo Han 003; Chen Gong 002; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2019)

Uncoupled Regression from Pairwise Comparison Data. - Liyuan Xu; Junya Honda; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2019)

Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. - Miao Xu 001; Bingcong Li; Gang Niu 001; Bo Han 003; Masashi Sugiyama (CoRR, 2019)

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. - Feng Liu 003; Jie Lu 001; Bo Han 003; Gang Niu 001; Guangquan Zhang 001; Masashi Sugiyama (CoRR, 2019)

Searching to Exploit Memorization Effect in Learning from Corrupted Labels. - Hansi Yang; Quanming Yao; Bo Han 003; Gang Niu 001 (CoRR, 2019)

Where is the Bottleneck of Adversarial Learning with Unlabeled Data? - Jingfeng Zhang; Bo Han 003; Gang Niu 001; Tongliang Liu; Masashi Sugiyama (CoRR, 2019)

Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. - Chao Li 013; Mohammad Emtiyaz Khan; Zhun Sun; Gang Niu 001; Bo Han 003; Shengli Xie; Qibin Zhao (AAAI, 2020)

Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. - Nan Lu; Tianyi Zhang; Gang Niu 001; Masashi Sugiyama (AISTATS, 2020)

Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. - Chun Wang; Bo Han 003; Shirui Pan; Jing Jiang 002; Gang Niu 001; Guodong Long (ICDM, 2020)

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. - Bo Han 003; Gang Niu 001; Xingrui Yu; Quanming Yao; Miao Xu 001; Ivor W. Tsang; Masashi Sugiyama (ICML, 2020)

Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. - Yu-Ting Chou; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (ICML, 2020)

Learning with Multiple Complementary Labels. - Lei Feng 006; Takuo Kaneko; Bo Han 003; Gang Niu 001; Bo An 001; Masashi Sugiyama (ICML, 2020)

Do We Need Zero Training Loss After Achieving Zero Training Error? - Takashi Ishida 001; Ikko Yamane; Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (ICML, 2020)

Progressive Identification of True Labels for Partial-Label Learning. - Jiaqi Lv; Miao Xu 001; Lei Feng 006; Gang Niu 001; Xin Geng 001; Masashi Sugiyama (ICML, 2020)

Searching to Exploit Memorization Effect in Learning with Noisy Labels. - Quanming Yao; Hansi Yang; Bo Han 003; Gang Niu 001; James Tin-Yau Kwok (ICML, 2020)

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Gang Niu 001; Lizhen Cui; Masashi Sugiyama; Mohan S. Kankanhalli (ICML, 2020)

Rethinking Importance Weighting for Deep Learning under Distribution Shift. - Tongtong Fang; Nan Lu; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2020)

Provably Consistent Partial-Label Learning. - Lei Feng 006; Jiaqi Lv; Bo Han 003; Miao Xu 001; Gang Niu 001; Xin Geng 001; Bo An 001; Masashi Sugiyama (NeurIPS, 2020)

Part-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (NeurIPS, 2020)

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Jiankang Deng; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2020)

Towards Mixture Proportion Estimation without Irreducibility. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Dacheng Tao (CoRR, 2020)

Multi-Class Classification from Noisy-Similarity-Labeled Data. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (CoRR, 2020)

Class2Simi: A New Perspective on Learning with Label Noise. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (CoRR, 2020)

Parts-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (CoRR, 2020)

Maximum Mean Discrepancy is Aware of Adversarial Attacks. - Ruize Gao; Feng Liu 003; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Masashi Sugiyama (CoRR, 2020)

A Survey of Label-noise Representation Learning: Past, Present and Future. - Bo Han 003; Quanming Yao; Tongliang Liu; Gang Niu 001; Ivor W. Tsang; James T. Kwok; Masashi Sugiyama (CoRR, 2020)

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. - Qizhou Wang; Bo Han 003; Tongliang Liu; Gang Niu 001; Jian Yang 003; Chen Gong 002 (AAAI, 2021)

Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. - Alon Jacovi; Gang Niu 001; Yoav Goldberg; Masashi Sugiyama (EACL, 2021)

Geometry-aware Instance-reweighted Adversarial Training. - Jingfeng Zhang; Jianing Zhu; Gang Niu 001; Bo Han 003; Masashi Sugiyama; Mohan S. Kankanhalli (ICLR, 2021)

Large-Margin Contrastive Learning with Distance Polarization Regularizer. - Shuo Chen 003; Gang Niu 001; Chen Gong 002; Jun Li 027; Jian Yang 003; Masashi Sugiyama (ICML, 2021)

Confidence Scores Make Instance-dependent Label-noise Learning Possible. - Antonin Berthon; Bo Han 003; Gang Niu 001; Tongliang Liu; Masashi Sugiyama (ICML, 2021)

Learning from Similarity-Confidence Data. - Yuzhou Cao; Lei Feng 006; Yitian Xu; Bo An 001; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Learning Diverse-Structured Networks for Adversarial Robustness. - Xuefeng Du; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Yu Rong 001; Gang Niu 001; Junzhou Huang; Masashi Sugiyama (ICML, 2021)

Pointwise Binary Classification with Pairwise Confidence Comparisons. - Lei Feng 006; Senlin Shu; Nan Lu; Bo Han 003; Miao Xu 001; Gang Niu 001; Bo An 001; Masashi Sugiyama (ICML, 2021)

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. - Ruize Gao; Feng Liu 003; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Provably End-to-end Label-noise Learning without Anchor Points. - Xuefeng Li; Tongliang Liu; Bo Han 003; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. - Nan Lu; Shida Lei; Gang Niu 001; Issei Sato; Masashi Sugiyama (ICML, 2021)

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (ICML, 2021)

CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. - Hanshu Yan; Jingfeng Zhang; Gang Niu 001; Jiashi Feng; Vincent Y. F. Tan; Masashi Sugiyama (ICML, 2021)

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. - Yivan Zhang; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Multiple-Instance Learning from Similar and Dissimilar Bags. - Lei Feng 006; Senlin Shu; Yuzhou Cao; Lue Tao; Hongxin Wei; Tao Xiang 001; Bo An 001; Gang Niu 001 (KDD, 2021)

Understanding and Improving Early Stopping for Learning with Noisy Labels. - Yingbin Bai; Erkun Yang; Bo Han 003; Yanhua Yang; Jiatong Li; Yinian Mao; Gang Niu 001; Tongliang Liu (NeurIPS, 2021)

Probabilistic Margins for Instance Reweighting in Adversarial Training. - Qizhou Wang; Feng Liu 003; Bo Han 003; Tongliang Liu; Chen Gong 002; Gang Niu 001; Mingyuan Zhou; Masashi Sugiyama (NeurIPS, 2021)

Instance-dependent Label-noise Learning under a Structural Causal Model. - Yu Yao 005; Tongliang Liu; Mingming Gong; Bo Han 003; Gang Niu 001; Kun Zhang 001 (NeurIPS, 2021)

Understanding the Interaction of Adversarial Training with Noisy Labels. - Jianing Zhu; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Hongxia Yang; Mohan S. Kankanhalli; Masashi Sugiyama (CoRR, 2021)

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Gang Niu 001; Bo Han 003 (CoRR, 2021)

Guided Interpolation for Adversarial Training. - Chen Chen 043; Jingfeng Zhang; Xilie Xu; Tianlei Hu; Gang Niu 001; Gang Chen 001; Masashi Sugiyama (CoRR, 2021)

Estimating Instance-dependent Label-noise Transition Matrix using DNNs. - Shuo Yang 006; Erkun Yang; Bo Han 003; Yang Liu 018; Min Xu 001; Gang Niu 001; Tongliang Liu (CoRR, 2021)

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Gang Niu 001; Lizhen Cui; Masashi Sugiyama (CoRR, 2021)

Instance Correction for Learning with Open-set Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2021)

Understanding (Generalized) Label Smoothing when Learning with Noisy Labels. - Jiaheng Wei; Hangyu Liu; Tongliang Liu; Gang Niu 001; Yang Liu 018 (CoRR, 2021)

On the Robustness of Average Losses for Partial-Label Learning. - Jiaqi Lv; Lei Feng 006; Miao Xu 001; Bo An 001; Gang Niu 001; Xin Geng 001; Masashi Sugiyama (CoRR, 2021)

Multi-Class Classification from Single-Class Data with Confidences. - Yuzhou Cao; Lei Feng 006; Senlin Shu; Yitian Xu; Bo An 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2021)

Local Reweighting for Adversarial Training. - Ruize Gao; Feng Liu 003; Kaiwen Zhou; Gang Niu 001; Bo Han 003; James Cheng (CoRR, 2021)

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. - Cheng-Yu Hsieh; Wei-I Lin; Miao Xu 001; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (CoRR, 2021)

Information-Theoretic Representation Learning for Positive-Unlabeled Classification. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2021)

Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. - Wenkai Xu; Gang Niu 001; Aapo Hyvärinen; Masashi Sugiyama (Neural Comput., 2021)

Learning and Mining with Noisy Labels. - Masashi Sugiyama; Tongliang Liu; Bo Han 003; Yang Liu 018; Gang Niu 001 (CIKM, 2022)

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. - De Cheng; Tongliang Liu; Yixiong Ning; Nannan Wang 001; Bo Han 003; Gang Niu 001; Xinbo Gao 001; Masashi Sugiyama (CVPR, 2022)

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Bo Han 003; Gang Niu 001; Mingyuan Zhou; Masashi Sugiyama (ICLR, 2022)

Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. - Nan Lu; Zhao Wang 006; Xiaoxiao Li; Gang Niu 001; Qi Dou 001; Masashi Sugiyama (ICLR, 2022)

PiCO: Contrastive Label Disambiguation for Partial Label Learning. - Haobo Wang 001; Ruixuan Xiao; Yixuan Li 001; Lei Feng 006; Gang Niu 001; Gang Chen 001; Junbo Zhao 002 (ICLR, 2022)

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. - Jiaheng Wei; Zhaowei Zhu; Hao Cheng 005; Tongliang Liu; Gang Niu 001; Yang Liu 018 (ICLR, 2022)

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (ICLR, 2022)

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Dacheng Tao (ICLR, 2022)

Exploiting Class Activation Value for Partial-Label Learning. - Fei Zhang; Lei Feng 006; Bo Han 003; Tongliang Liu; Gang Niu 001; Tao Qin; Masashi Sugiyama (ICLR, 2022)

Adversarial Robustness Through the Lens of Causality. - Yonggang Zhang 003; Mingming Gong; Tongliang Liu; Gang Niu 001; Xinmei Tian 001; Bo Han 003; Bernhard Schölkopf; Kun Zhang 001 (ICLR, 2022)

Reliable Adversarial Distillation with Unreliable Teachers. - Jianing Zhu; Jiangchao Yao; Bo Han 003; Jingfeng Zhang; Tongliang Liu; Gang Niu 001; Jingren Zhou 001; Jianliang Xu; Hongxia Yang (ICLR, 2022)

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. - Ruize Gao; Jiongxiao Wang; Kaiwen Zhou; Feng Liu 003; Binghui Xie; Gang Niu 001; Bo Han 003; James Cheng (ICML, 2022)

To Smooth or Not? When Label Smoothing Meets Noisy Labels. - Jiaheng Wei; Hangyu Liu; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Yang Liu 018 (ICML, 2022)

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. - Shuo Yang 006; Erkun Yang; Bo Han 003; Yang Liu 018; Min Xu 001; Gang Niu 001; Tongliang Liu (ICML, 2022)

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. - Jianan Zhou 002; Jianing Zhu; Jingfeng Zhang; Tongliang Liu; Gang Niu 001; Bo Han 003; Masashi Sugiyama (NeurIPS, 2022)

Learning Contrastive Embedding in Low-Dimensional Space. - Shuo Chen 003; Chen Gong 002; Jun Li 027; Jian Yang 003; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2022)

Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. - Yuzhou Cao; Tianchi Cai; Lei Feng 006; Lihong Gu; Jinjie Gu; Bo An 001; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2022)

FedMT: Federated Learning with Mixed-type Labels. - Qiong Zhang; Aline Talhouk; Gang Niu 001; Xiaoxiao Li (CoRR, 2022)

Logit Clipping for Robust Learning against Label Noise. - Hongxin Wei; Huiping Zhuang; Renchunzi Xie; Lei Feng 006; Gang Niu 001; Bo An 001; Yixuan Li 001 (CoRR, 2022)

Fast and Robust Rank Aggregation against Model Misspecification. - Yuangang Pan; Ivor W. Tsang; Weijie Chen 001; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2022)

Learning from Noisy Pairwise Similarity and Unlabeled Data. - Songhua Wu; Tongliang Liu; Bo Han 003; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2022)

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. - Zhuowei Wang 003; Jing Jiang 002; Bo Han 003; Lei Feng 006; Bo An 001; Gang Niu 001; Guodong Long (Trans. Mach. Learn. Res., 2022)

NoiLin: Improving adversarial training and correcting stereotype of noisy labels. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Lizhen Cui; Gang Niu 001; Masashi Sugiyama (Trans. Mach. Learn. Res., 2022)

Towards Effective Visual Representations for Partial-Label Learning. - Shiyu Xia; Jiaqi Lv; Ning Xu 009; Gang Niu 001; Xin Geng 001 (CVPR, 2023)

Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. - Jialiang Tang; Shuo Chen 003; Gang Niu 001; Masashi Sugiyama; Chen Gong 002 (ICCV, 2023)

Multi-Label Knowledge Distillation. - Penghui Yang 001; Ming-Kun Xie; Chen-Chen Zong; Lei Feng 006; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICCV, 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 001; Ikko Yamane; Nontawat Charoenphakdee; Gang Niu 001; Masashi Sugiyama (ICLR, 2023)

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. - Ruijiang Dong; Feng Liu 003; Haoang Chi; Tongliang Liu; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (ICML, 2023)

A Universal Unbiased Method for Classification from Aggregate Observations. - Zixi Wei; Lei Feng 006; Bo Han 003; Tongliang Liu; Gang Niu 001; Xiaofeng Zhu 001; Heng Tao Shen (ICML, 2023)

Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. - Hongxin Wei; Huiping Zhuang; Renchunzi Xie; Lei Feng 006; Gang Niu 001; Bo An 001; Yixuan Li 001 (ICML, 2023)

Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. - Tongtong Fang; Nan Lu; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2023)

Binary Classification with Confidence Difference. - Wei Wang 373; Lei Feng 006; Yuchen Jiang; Gang Niu 001; Min-Ling Zhang; 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 001; Masashi Sugiyama; Sheng-Jun Huang (NeurIPS, 2023)

Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration. - Jie Xu 044; Shuo Chen 003; Yazhou Ren 001; Xiaoshuang Shi; Hengtao Shen; Gang Niu 001; Xiaofeng Zhu 001 (NeurIPS, 2023)

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. - Jianing Zhu; Yu Geng; Jiangchao Yao; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (NeurIPS, 2023)

Fairness Improves Learning from Noisily Labeled Long-Tailed Data. - Jiaheng Wei; Zhaowei Zhu; Gang Niu 001; Tongliang Liu; Sijia Liu 001; Masashi Sugiyama; Yang Liu 018 (CoRR, 2023)

Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios. - Jie Xu 044; Gang Niu 001; Xiaolong Wang; Yazhou Ren 001; Lei Feng 006; Xiaoshuang Shi; Heng Tao Shen; Xiaofeng Zhu 001 (CoRR, 2023)

Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. - Jingfeng Zhang; Bo Song; Bo Han 003; Lei Liu 003; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. - Wei-I Lin; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (CoRR, 2023)

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. - Yuhao Wu; Xiaobo Xia; Jun Yu 001; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Tongliang Liu (CoRR, 2023)

Atom-Motif Contrastive Transformer for Molecular Property Prediction. - Wentao Yu; Shuo Chen 003; Chen Gong 002; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. - Jianing Zhu; Geng Yu; Jiangchao Yao; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (CoRR, 2023)

Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. - Wei Wang 373; Takashi Ishida 001; Yu-Jie Zhang; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Representation learning for continuous action spaces is beneficial for efficient policy learning. - Tingting Zhao 001; Ying Wang; Wei Sun; Yarui Chen; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2023)

Class-Wise Denoising for Robust Learning Under Label Noise. - Chen Gong 002; Yongliang Ding; Bo Han 003; Gang Niu 001; Jian Yang 003; Jane You; Dacheng Tao; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning Intention-Aware Policies in Deep Reinforcement Learning. - Tingting Zhao 001; S. Wu; G. Li; Y. Chen; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2023)

Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity. - Lei Feng 006; Senlin Shu; Yuzhou Cao; Lue Tao; Hongxin Wei; Tao Xiang 001; Bo An 001; Gang Niu 001 (IEEE Trans. Knowl. Data Eng., 2023)

Boundary-restricted metric learning. - Shuo Chen 003; Chen Gong 002; Xiang Li 041; Jian Yang 003; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2023)

A Parametrical Model for Instance-Dependent Label Noise. - Shuo Yang 006; Songhua Wu; Erkun Yang; Bo Han 003; Yang Liu 018; Min Xu 001; Gang Niu 001; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios. - Jie Xu 044; Yazhou Ren 001; Xiaolong Wang; Lei Feng 006; Zheng Zhang 006; Gang Niu 001; Xiaofeng Zhu 001 (CVPR, 2024)

Direct Distillation Between Different Domains. - Jialiang Tang; Shuo Chen 003; Gang Niu 001; Hongyuan Zhu; Joey Tianyi Zhou; Chen Gong 002; Masashi Sugiyama (ECCV (80), 2024)

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. - Jiahao Xiao; Ming-Kun Xie; Heng-Bo Fan; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ECCV (52), 2024)

Robust Similarity Learning with Difference Alignment Regularization. - Shuo Chen 003; Gang Niu 001; Chen Gong 002; Okan Koc; Jian Yang 003; Masashi Sugiyama (ICLR, 2024)

Accurate Forgetting for Heterogeneous Federated Continual Learning. - Abudukelimu Wuerkaixi; Sen Cui; Jingfeng Zhang; Kunda Yan; Bo Han 003; Gang Niu 001; Lei Fang; Changshui Zhang; Masashi Sugiyama (ICLR, 2024)

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. - Ziqing Fan; Shengchao Hu; Jiangchao Yao; Gang Niu 001; Ya Zhang 002; Masashi Sugiyama; Yanfeng Wang 001 (ICML, 2024)

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. - Wei Wang 373; Takashi Ishida 001; Yu-Jie Zhang; Gang Niu 001; Masashi Sugiyama (ICML, 2024)

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. - Ming-Kun Xie; Jiahao Xiao; Pei Peng 005; Gang Niu 001; Masashi Sugiyama; Sheng-Jun Huang (ICML, 2024)

Balancing Similarity and Complementarity for Federated Learning. - Kunda Yan; Sen Cui; Abudukelimu Wuerkaixi; Jingfeng Zhang; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Changshui Zhang (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 001; Masashi Sugiyama (ICML, 2024)

Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. - Zhikang Chen; Min Zhang 005; Sen Cui; Haoxuan Li 001; Gang Niu 001; Mingming Gong; Changshui Zhang; Kun Zhang 001 (NeurIPS, 2024)

What Makes Partial-Label Learning Algorithms Effective? - Jiaqi Lv; Yangfan Liu; Shiyu Xia; Ning Xu 009; Miao Xu 001; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama; Xin Geng 001 (NeurIPS, 2024)

Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. - Zhen-Yu Zhang; Siwei Han; Huaxiu Yao; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

Decoupling the Class Label and the Target Concept in Machine Unlearning. - Jianing Zhu; Bo Han 003; Jiangchao Yao; Jianliang Xu; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

On Unsupervised Prompt Learning for Classification with Black-box Language Models. - Zhen-Yu Zhang; Jiandong Zhang; Huaxiu Yao; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels. - Yuxin Tian; Mouxing Yang; Yuhao Zhou; Jian Wang 124; Qing Ye; Tongliang Liu; Gang Niu 001; Jiancheng Lv 001 (CoRR, 2024)

Learning explainable task-relevant state representation for model-free deep reinforcement learning. - Tingting Zhao 001; Guixi Li; Tuo Zhao; Yarui Chen; Ning Xie 003; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2024)

On the Robustness of Average Losses for Partial-Label Learning. - Jiaqi Lv; Biao Liu; Lei Feng 006; Ning Xu 009; Miao Xu 001; Bo An 001; Gang Niu 001; Xin Geng 001; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning. - Haobo Wang 001; Ruixuan Xiao; Yixuan Li 001; Lei Feng 006; Gang Niu 001; Gang Chen 001; Junbo Zhao 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

On the Effectiveness of Adversarial Training Against Backdoor Attacks. - Yinghua Gao; Dongxian Wu; Jingfeng Zhang; Guanhao Gan; Shu-Tao Xia; Gang Niu 001; Masashi Sugiyama (IEEE Trans. Neural Networks Learn. Syst., 2024)

Realistic Evaluation of Deep Partial-Label Learning Algorithms. - Wei Wang 373; Dong-Dong Wu; Jindong Wang 001; Gang Niu 001; Min-Ling Zhang; Masashi Sugiyama (ICLR, 2025)

Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. - Zhuo Huang; Gang Niu 001; Bo Han 003; Masashi Sugiyama; Tongliang Liu (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 001; Masashi Sugiyama; Yang Yu 001 (ICLR, 2025)

Estimating Per-Class Statistics for Label Noise Learning. - Wenshui Luo; Shuo Chen 003; Tongliang Liu; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Dacheng Tao; Chen Gong 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. - Jie Xu 044; Na Zhao; Gang Niu 001; Masashi Sugiyama; Xiaofeng Zhu 001 (CoRR, 2025)

Learning without Isolation: Pathway Protection for Continual Learning. - Zhikang Chen; Abudukelimu Wuerkaixi; Sen Cui; Haoxuan Li 001; Ding Li; Jingfeng Zhang; Bo Han 003; Gang Niu 001; Houfang Liu; Yi Yang 039; Sifan Yang; Changshui Zhang; et al. (CoRR, 2025)

On the Role of Label Noise in the Feature Learning Process. - Andi Han; Wei Huang 034; Zhanpeng Zhou; Gang Niu 001; Wuyang Chen 001; Junchi Yan; Akiko Takeda; Taiji Suzuki (CoRR, 2025)

What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? - Ming-Kun Xie; Jiahao Xiao; Gang Niu 001; Lei Feng 006; Zhiqiang Kou; Min-Ling Zhang; Masashi Sugiyama (CoRR, 2025)