日本の大学への公開時点の所属を確認できた著者表記を掲載しています。大学IDと完全一致の著者名でまとめており、人物の名寄せはしていません。同名別人や表記違いの可能性があり、現在所属・全業績・研究者の総合順位を表すものではありません。
2025 · AAAI
Action-Agnostic Point-Level Supervision for Temporal Action DetectionShuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama
AI (General)
日本大学: 東北大学 / 東京大学
2025 · ICCV
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated PerturbationJie Xu, Na Zhao, Gang Niu, Masashi Sugiyama, Xiaofeng Zhu
Computer Vision
日本大学: 東京大学
2025 · ICLR
Learning View-invariant World Models for Visual Robotic ManipulationJing-Cheng Pang, Nan Tang, Kaiyuan Li, Yuting Tang, Xin-Qiang Cai, Zhen-Yu Zhang, Gang Niu, Masashi Sugiyama, Yang Yu
Machine Learning
日本大学: 東京大学
2025 · ICLR
Realistic Evaluation of Deep Partial-Label Learning AlgorithmsWei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2025 · ICLR
Sharpness-Aware Black-Box OptimizationFeiyang YE, YUEMING LYU, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor Tsang
Machine Learning
日本大学: 東京大学
2025 · ICLR
Towards Effective Evaluations and Comparisons for LLM Unlearning MethodsQizhou Wang, Bo Han, Puning Yang, Jianing ZHU, Tongliang Liu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2025 · ICLR
Towards Out-of-Modal Generalization without Instance-level Modal CorrespondenceZhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu
Machine Learning
日本大学: 東京大学
2025 · ICML
Adaptive Localization of Knowledge Negation for Continual LLM UnlearningAbudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
Machine Learning
日本大学: 東京大学
2025 · ICML
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via MixabilityYu-Jie Zhang, Peng Zhao, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2025 · ICML
Parallel Simulation for Log-concave Sampling and Score-based Diffusion ModelsHuanjian Zhou, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2025 · IJCAI
Label Distribution Learning with Biased Annotations Assisted by Multi-Label LearningZhiqiang Kou, Si Qin, Hailin Wang, Jing Wang, Mingkun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng
AI (General)
日本大学: 東京大学
2025 · NeurIPS
Generalized Linear Bandits: Almost Optimal Regret with One-Pass UpdateYu-Jie Zhang, Sheng-An Xu, Peng Zhao, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2025 · NeurIPS
The Adaptive Complexity of Minimizing Relative Fisher InformationHuanjian Zhou, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · AAAI
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit ModelsJongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama
AI (General)
日本大学: 東京大学
2024 · AAAI
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed BanditShintaro Nakamura, Masashi Sugiyama
AI (General)
日本大学: 東京大学
2024 · ECCV
Direct Distillation between Different DomainsJialiang Tang, Shuo Chen*, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong*, Masashi Sugiyama
Computer Vision
日本大学: 東京大学
2024 · ECCV
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label LearningJia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang*
Computer Vision
日本大学: 東京大学
2024 · EMNLP
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient ModificationMing Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama
Natural Language Processing
日本大学: 東京大学
2024 · ICLR
Accurate Forgetting for Heterogeneous Federated Continual LearningAbudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · ICLR
Robust Similarity Learning with Difference Alignment RegularizationShuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · ICLR
Understanding and Mitigating the Label Noise in Pre-training on Downstream TasksHao Chen, Jindong Wang, Ankit Parag Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj
Machine Learning
日本大学: 東京大学
2024 · ICML
A General Framework for Learning from Weak SupervisionHao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
Machine Learning
日本大学: 東京科学大学 / 東京大学
2024 · ICML
Balancing Similarity and Complementarity for Federated LearningKunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
Machine Learning
日本大学: 東京大学
2024 · ICML
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingMing-Kun Xie, Jia-Hao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
Machine Learning
日本大学: 東京大学
2024 · ICML
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift AdaptationYu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou
Machine Learning
日本大学: 東京大学
2024 · ICML
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate ThoughtZhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · ICML
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More PracticalWei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · ICML
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware MinimizationZiqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang
Machine Learning
日本大学: 東京大学
2024 · ICRA
An offline learning of behavior correction policy for vision-based robotic manipulationQingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama
Robotics
日本大学: 東京大学
2024 · IJCAI
Towards Robust Multi-Label Learning against Dirty Label NoiseZhiqiang Kou, Si Qin, Hailin Wang, Jing Wang, Mingkun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng
AI (General)
日本大学: 東京大学
2024 · NeurIPS
Enriching Disentanglement: From Logical Definitions to Quantitative MetricsYivan Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · NeurIPS
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label ConfigurationsHao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xiang Li, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
Machine Learning
日本大学: 東京科学大学 / 東京大学
2024 · NeurIPS
Slight Corruption in Pre-training Data Makes Better Diffusion ModelsHao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj
Machine Learning
日本大学: 東京大学
2024 · NeurIPS
Test-time Adaptation in Non-stationary Environments via Adaptive Representation AlignmentZhen-Yu Zhang, Zhiyu Xie, Huaxiu Yao, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2024 · NeurIPS
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
Machine Learning
日本大学: 東京大学
2023 · ICCV
Distribution Shift Matters for Knowledge Distillation with Webly Collected ImagesJialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
Computer Vision
日本大学: 東京大学
2023 · ICCV
Multi-Label Knowledge DistillationPenghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
Computer Vision
日本大学: 東京大学
2023 · ICLR
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary ClassificationTakashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · ICLR
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation LearningXinqiang Cai, Yao-Xiang Ding, Zixuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou
Machine Learning
日本大学: 東京大学
2023 · ICML
A Category-theoretical Meta-analysis of Definitions of DisentanglementYivan Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · ICML
Diversity-enhancing Generative Network for Few-shot Hypothesis AdaptationRuijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
Machine Learning
日本大学: 東京大学
2023 · ICML
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksSalah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon
Machine Learning
日本大学: 東京大学
2023 · ICML
Optimality of Thompson Sampling with Noninformative Priors for Pareto BanditsJongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama
Machine Learning
日本大学: 京都大学 / 東京大学
2023 · NeurIPS
Adapting to Continuous Covariate Shift via Online Density Ratio EstimationYu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Binary Classification with Confidence DifferenceWei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label LearningMing-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Distributional Pareto-Optimal Multi-Objective Reinforcement LearningXin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama, Ashley Llorens
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative ExtrapolationJianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset SelectionXilie Xu, Jingfeng ZHANG, Feng Liu, Masashi Sugiyama, Mohan S Kankanhalli
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Enhancing Adversarial Contrastive Learning via Adversarial Invariant RegularizationXilie Xu, Jingfeng ZHANG, Feng Liu, Masashi Sugiyama, Mohan S Kankanhalli
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Generalizing Importance Weighting to A Universal Solver for Distribution Shift ProblemsTongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Imitation Learning from Vague FeedbackXin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm PerspectiveZeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2023 · NeurIPS
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation CostYu-Jie Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · CVPR
Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix EstimationDe Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama
Computer Vision
日本大学: 東京大学
2022 · ICLR
Exploiting Class Activation Value for Partial-Label LearningFei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · ICLR
Federated Learning from Only Unlabeled Data with Class-conditional-sharing ClientsNan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · ICLR
Meta Discovery: Learning to Discover Novel Classes given Very Limited DataHaoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · ICLR
Rethinking Class-Prior Estimation for Positive-Unlabeled LearningYu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
Machine Learning
日本大学: 東京大学
2022 · ICLR
Sample Selection with Uncertainty of Losses for Learning with Noisy LabelsXiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · ICML
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and MomentumZeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · ICML
Adversarial Attack and Defense for Non-Parametric Two-Sample TestsXilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli
Machine Learning
日本大学: 東京大学
2022 · ICML
To Smooth or Not? When Label Smoothing Meets Noisy LabelsJiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
Machine Learning
日本大学: 東京大学
2022 · IJCAI
Towards Adversarially Robust Deep Image DenoisingHanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan
AI (General)
日本大学: 東京大学
2022 · NeurIPS
Adapting to Online Label Shift with Provable GuaranteesYong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou
Machine Learning
日本大学: 東京大学
2022 · NeurIPS
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative AttacksJianan Zhou, Jianing Zhu, Jingfeng ZHANG, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · NeurIPS
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesYuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie GU, Bo An, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · NeurIPS
Learning Contrastive Embedding in Low-Dimensional SpaceShuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2022 · NeurIPS
Synergy-of-Experts: Collaborate to Improve Adversarial RobustnessSen Cui, Jingfeng ZHANG, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang
Machine Learning
日本大学: 東京大学
2021 · CVPR
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical PerspectiveNontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama
Computer Vision
日本大学: 東京科学大学 / 東京大学
2021 · ICLR
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat MinimaZeke Xie, Issei Sato, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICLR
Geometry-aware Instance-reweighted Adversarial TrainingJingfeng Zhang, Jianing ZHU, Gang Niu, Bo Han, Masashi Sugiyama, Mohan Kankanhalli
Machine Learning
日本大学: 東京大学
2021 · ICML
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set ClassificationNan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature SelectionHanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Tan, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Classification with Rejection Based on Cost-sensitive ClassificationNontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Confidence Scores Make Instance-dependent Label-noise Learning PossibleAntonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Large-Margin Contrastive Learning with Distance Polarization RegularizerShuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Learning Diverse-Structured Networks for Adversarial RobustnessXuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation RegularizationYivan Zhang, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Learning from Similarity-Confidence DataYuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Lower-Bounded Proper Losses for Weakly Supervised ClassificationShuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama
Machine Learning
日本大学: 公立はこだて未来大学 / 東京大学
2021 · ICML
Maximum Mean Discrepancy Test is Aware of Adversarial AttacksRuize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Mediated Uncoupled Learning: Learning Functions without Direct Input-output CorrespondencesIkko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama
Machine Learning
日本大学: 京都大学 / 東京大学
2021 · ICML
Pointwise Binary Classification with Pairwise Confidence ComparisonsLei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve GeneralizationZeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · ICML
Provably End-to-end Label-noise Learning without Anchor PointsXuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · NeurIPS
Loss function based second-order Jensen inequality and its application to particle variational inferenceFutoshi Futami, Tomoharu Iwata, naonori ueda, Issei Sato, Masashi Sugiyama
Machine Learning
日本大学: 東京大学
2021 · NeurIPS
Probabilistic Margins for Instance Reweighting in Adversarial Trainingqizhou wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Machine Learning
日本大学: 東京大学