Masashi Sugiyama - researchr alias

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Viewing Publication 1 - 100 from 498

2025

Estimating Per-Class Statistics for Label Noise LearningWenshui Luo, Shuo Chen 0003, Tongliang Liu, Bo Han 0003, Gang Niu 0001, Masashi Sugiyama, Dacheng Tao, Chen Gong 0002. pami, 47(1):305-322, January 2025. [doi]

Action-Agnostic Point-Level Supervision for Temporal Action DetectionShuhei M. Yoshida, Takashi Shibata 0001, Makoto Terao, Takayuki Okatani, Masashi Sugiyama. AAAI 2025: 9571-9579 [doi]

2024

Learning explainable task-relevant state representation for model-free deep reinforcement learningTingting Zhao 0001, Guixi Li, Tuo Zhao, Yarui Chen, Ning Xie 0003, Gang Niu 0001, Masashi Sugiyama. NN, 180:106741, 2024. [doi]

Robust Similarity Learning with Difference Alignment RegularizationShuo Chen 0003, Gang Niu 0001, Chen Gong 0002, Okan Koc, Jian Yang 0003, Masashi Sugiyama. iclr 2024: [doi]

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label ConfigurationsHao Chen 0102, Ankit Shah 0001, Jindong Wang 0001, Ran Tao 0013, Yidong Wang, Xiang Li 0106, Xing Xie 0001, Masashi Sugiyama, Rita Singh, Bhiksha Raj. nips 2024: [doi]

A General Framework for Learning from Weak SupervisionHao Chen 0102, Jindong Wang 0001, Lei Feng, Xiang Li 0106, Yidong Wang, Xing Xie 0001, Masashi Sugiyama, Rita Singh, Bhiksha Raj. icml 2024: [doi]

Understanding and Mitigating the Label Noise in Pre-training on Downstream TasksHao Chen 0102, Jindong Wang 0001, Ankit Shah 0001, Ran Tao 0013, Hongxin Wei, Xing Xie 0001, Masashi Sugiyama, Bhiksha Raj. iclr 2024: [doi]

Offline Reinforcement Learning from Datasets with Structured Non-StationarityJohannes Ackermann, Takayuki Osa, Masashi Sugiyama. rlc 2024: 2140-2161

VEC-SBM: Optimal Community Detection with Vectorial Edges CovariatesGuillaume Braun, Masashi Sugiyama. aistats 2024: 532-540 [doi]

Slight Corruption in Pre-training Data Makes Better Diffusion ModelsHao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu 0010, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj. nips 2024: [doi]

An offline learning of behavior correction policy for vision-based robotic manipulationQingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama. icra 2024: 5448-5454 [doi]

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware MinimizationZiqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu 0001, Ya Zhang 0002, Masashi Sugiyama, Yanfeng Wang. icml 2024: [doi]

On the Effectiveness of Adversarial Training Against Backdoor AttacksYinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu 0001, Masashi Sugiyama. tnn, 35(10):14878-14888, October 2024. [doi]

The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit ModelsJongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama. AAAI 2024: 13383-13390 [doi]

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient ModificationMing Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama. emnlp 2024: 14394-14410 [doi]

On the Robustness of Average Losses for Partial-Label LearningJiaqi Lv, Biao Liu, Lei Feng 0006, Ning Xu 0009, Miao Xu, Bo An 0001, Gang Niu 0001, Xin Geng 0001, Masashi Sugiyama. pami, 46(5):2569-2583, May 2024. [doi]

What Makes Partial-Label Learning Algorithms Effective?Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu 0009, Miao Xu, Gang Niu 0001, Min-Ling Zhang, Masashi Sugiyama, Xin Geng 0001. nips 2024: [doi]

Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed BanditShintaro Nakamura, Masashi Sugiyama. AAAI 2024: 14414-14421 [doi]

Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed BanditShintaro Nakamura, Masashi Sugiyama. aistats 2024: 1225-1233 [doi]

Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift AdaptationYuyang Qian, Peng Zhao 0006, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou. icml 2024: [doi]

Overcoming Continuous Distribution Shifts: Challenges in Online Machine LearningMasashi Sugiyama. bci3 2024: 1-3 [doi]

Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled DataYuki Tanaka, Shuhei M. Yoshida, Takashi Shibata 0001, Makoto Terao, Takayuki Okatani, Masashi Sugiyama. wacv 2024: 2768-2777 [doi]

Direct Distillation Between Different DomainsJialiang Tang, Shuo Chen 0003, Gang Niu 0001, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong 0002, Masashi Sugiyama. eccv 2025: 154-172 [doi]

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More PracticalWei Wang, Takashi Ishida 0001, Yu-Jie Zhang, Gang Niu 0001, Masashi Sugiyama. icml 2024: [doi]

Accurate Forgetting for Heterogeneous Federated Continual LearningAbudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han 0003, Gang Niu 0001, Lei Fang, Changshui Zhang, Masashi Sugiyama. iclr 2024: [doi]

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label LearningJiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu 0001, Masashi Sugiyama, Sheng-Jun Huang. eccv 2025: 437-454 [doi]

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingMing-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu 0001, Masashi Sugiyama, Sheng-Jun Huang. icml 2024: [doi]

Balancing Similarity and Complementarity for Federated LearningKunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han 0003, Gang Niu 0001, Masashi Sugiyama, Changshui Zhang. icml 2024: [doi]

Enriching Disentanglement: From Logical Definitions to Quantitative MetricsYivan Zhang, Masashi Sugiyama. nips 2024: [doi]

Test-time Adaptation in Non-stationary Environments via Adaptive Representation AlignmentZhen-yu Zhang, Zhiyu Xie, Huaxiu Yao, Masashi Sugiyama. nips 2024: [doi]

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 0001, Masashi Sugiyama. icml 2024: [doi]

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise LearningJingfeng Zhang, Bo Song, Haohan Wang, Bo Han 0003, Tongliang Liu, Lei Liu 0003, Masashi Sugiyama. pami, 46(6):4398-4409, June 2024. [doi]

2023

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary ClassificationTakashi Ishida 0001, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu 0001, Masashi Sugiyama. iclr 2023: [doi]

Learning Intention-Aware Policies in Deep Reinforcement LearningTingting Zhao 0001, S. Wu, G. Li, Y. Chen, Gang Niu 0001, Masashi Sugiyama. neco, 35(10):1657-1677, October 2023. [doi]

Representation learning for continuous action spaces is beneficial for efficient policy learningTingting Zhao 0001, Ying Wang, Wei Sun, Yarui Chen, Gang Niu 0001, Masashi Sugiyama. NN, 159:137-152, February 2023. [doi]

Class-Wise Denoising for Robust Learning Under Label NoiseChen Gong 0002, Yongliang Ding, Bo Han 0003, Gang Niu 0001, Jian Yang 0003, Jane You, Dacheng Tao, Masashi Sugiyama. pami, 45(3):2835-2848, March 2023. [doi]

Imitation Learning from Vague FeedbackXin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama. nips 2023: [doi]

Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation LearningXin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang 0001, Masashi Sugiyama, Zhi-Hua Zhou. iclr 2023: [doi]

Distributional Pareto-Optimal Multi-Objective Reinforcement LearningXin-Qiang Cai, Pushi Zhang, Li Zhao 0007, Jiang Bian 0002, Masashi Sugiyama, Ashley Llorens. nips 2023: [doi]

Boundary-restricted metric learningShuo Chen, Chen Gong 0002, Xiang Li, Jian Yang 0003, Gang Niu 0001, Masashi Sugiyama. ml, 112(12):4723-4762, December 2023. [doi]

Diversity-enhancing Generative Network for Few-shot Hypothesis AdaptationRuijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu 0001, Masashi Sugiyama, Bo Han 0003. icml 2023: 8260-8275 [doi]

Generalizing Importance Weighting to A Universal Solver for Distribution Shift ProblemsTongtong Fang, Nan Lu, Gang Niu 0001, Masashi Sugiyama. nips 2023: [doi]

GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksSalah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon. icml 2023: 11255-11282 [doi]

Universal Approximation Property of Invertible Neural NetworksIsao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama. jmlr, 24, 2023. [doi]

Audio Signal Enhancement with Learning from Positive and Unlabeled DataNobutaka Ito, Masashi Sugiyama. icassp 2023: 1-5 [doi]

Optimality of Thompson Sampling with Noninformative Priors for Pareto BanditsJongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama. icml 2023: 18810-18851 [doi]

Thompson Exploration with Best Challenger Rule in Best Arm IdentificationJongyeong Lee, Junya Honda, Masashi Sugiyama. acml 2023: 646-661 [doi]

Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimationShota Nakajima, Masashi Sugiyama. ml, 112(3):889-919, March 2023. [doi]

Root cause estimation of faults in production processes: a novel approach inspired by approximate Bayesian computationYosuke Otsubo, Naoya Otani, Megumi Chikasue, Mineyuki Nishino, Masashi Sugiyama. ijpr, 61(5):1556-1574, March 2023. [doi]

Distribution Shift Matters for Knowledge Distillation with Webly Collected ImagesJialiang Tang, Shuo Chen 0003, Gang Niu 0001, Masashi Sugiyama, Chen Gong 0002. iccv 2023: 17424-17434 [doi]

Binary Classification with Confidence DifferenceWei Wang, Lei Feng, Yuchen Jiang, Gang Niu 0001, Min-Ling Zhang, Masashi Sugiyama. nips 2023: [doi]

Learning With Proper Partial LabelsZhenguo Wu, Jiaqi Lv, Masashi Sugiyama. neco, 35(1):58-81, January 2023. [doi]

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. nips 2023: [doi]

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label LearningMing-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu 0001, Masashi Sugiyama, Sheng-Jun Huang. nips 2023: [doi]

Enhancing Adversarial Contrastive Learning via Adversarial Invariant RegularizationXilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli. nips 2023: [doi]

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset SelectionXilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli. nips 2023: [doi]

Multi-Label Knowledge DistillationPenghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng 0006, Gang Niu 0001, Masashi Sugiyama, Sheng-Jun Huang. iccv 2023: 17225-17234 [doi]

A Category-theoretical Meta-analysis of Definitions of DisentanglementYivan Zhang, Masashi Sugiyama. icml 2023: 41596-41612 [doi]

Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation CostYu-Jie Zhang, Masashi Sugiyama. nips 2023: [doi]

Adapting to Continuous Covariate Shift via Online Density Ratio EstimationYu-Jie Zhang, Zhen-yu Zhang, Peng Zhao, Masashi Sugiyama. nips 2023: [doi]

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative ExtrapolationJianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu 0001, Masashi Sugiyama, Bo Han. nips 2023: [doi]

2022

Pairwise Supervision Can Provably Elicit a Decision BoundaryHan Bao 0002, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama. aistats 2022: 2618-2640 [doi]

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative AttacksJianan Zhou 0002, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu 0001, Bo Han 0003, Masashi Sugiyama. nips 2022: [doi]

Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised LearningChen Gong 0002, Jian Yang 0003, Jane You, Masashi Sugiyama. pami, 44(6):2841-2855, 2022. [doi]

Learning Contrastive Embedding in Low-Dimensional SpaceShuo Chen 0003, Chen Gong 0002, Jun Li 0027, Jian Yang 0003, Gang Niu 0001, Masashi Sugiyama. nips 2022: [doi]

Adapting to Online Label Shift with Provable GuaranteesYong Bai, Yu-Jie Zhang, Peng Zhao 0006, Masashi Sugiyama, Zhi-Hua Zhou. nips 2022: [doi]

Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesYuzhou Cao, Tianchi Cai, Lei Feng 0006, Lihong Gu, Jinjie Gu, Bo An 0001, Gang Niu 0001, Masashi Sugiyama. nips 2022: [doi]

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix EstimationDe Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang 0001, Bo Han 0003, Gang Niu 0001, Xinbo Gao 0001, Masashi Sugiyama. cvpr 2022: 16609-16618 [doi]

Meta Discovery: Learning to Discover Novel Classes given Very Limited DataHaoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han 0003, Gang Niu 0001, Mingyuan Zhou, Masashi Sugiyama. iclr 2022: [doi]

Synergy-of-Experts: Collaborate to Improve Adversarial RobustnessSen Cui, Jingfeng Zhang, Jian Liang, Bo Han 0003, Masashi Sugiyama, Changshui Zhang. nips 2022: [doi]

Neural Networks special issue on Artificial Intelligence and Brain ScienceKenji Doya, Karl J. Friston, Masashi Sugiyama, Joshua B. Tenenbaum. NN, 155:328-329, 2022. [doi]

Predictive variational Bayesian inference as risk-seeking optimizationFutoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama. aistats 2022: 5051-5083 [doi]

Learning from Noisy Complementary Labels with Robust Loss FunctionsHiroki Ishiguro, Takashi Ishida 0001, Masashi Sugiyama. ieicetd, 105(2):364-376, 2022. [doi]

LocalDrop: A Hybrid Regularization for Deep Neural NetworksZiqing Lu, Chang Xu 0002, Bo Du 0001, Takashi Ishida 0001, Lefei Zhang, Masashi Sugiyama. pami, 44(7):3590-3601, 2022. [doi]

Federated Learning from Only Unlabeled Data with Class-conditional-sharing ClientsNan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu 0001, Qi Dou, Masashi Sugiyama. iclr 2022: [doi]

Deep learning, reinforcement learning, and world modelsYutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto. NN, 152:267-275, 2022. [doi]

Robust computation of optimal transport by β-potential regularizationShintaro Nakamura, Han Bao 0002, Masashi Sugiyama. acml 2022: 770-785 [doi]

Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual informationTakayuki Osa, Voot Tangkaratt, Masashi Sugiyama. NN, 152:90-104, 2022. [doi]

Fast and Robust Rank Aggregation against Model MisspecificationYuangang Pan, Ivor W. Tsang, Weijie Chen 0001, Gang Niu 0001, Masashi Sugiyama. jmlr, 23, 2022. [doi]

Learning and Mining with Noisy LabelsMasashi Sugiyama, Tongliang Liu, Bo Han 0003, Yang Liu, Gang Niu 0001. CIKM 2022: 5152-5155 [doi]

Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk RegularizationYuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama. acml 2022: 990-1005 [doi]

Improving imbalanced classification using near-miss instancesAkira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima. eswa, 201:117130, 2022. [doi]

To Smooth or Not? When Label Smoothing Meets Noisy LabelsJiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu 0001, Masashi Sugiyama, Yang Liu. icml 2022: 23589-23614 [doi]

Learning from Noisy Pairwise Similarity and Unlabeled DataSonghua Wu, Tongliang Liu, Bo Han 0003, Jun Yu 0001, Gang Niu 0001, Masashi Sugiyama. jmlr, 23, 2022. [doi]

Sample Selection with Uncertainty of Losses for Learning with Noisy LabelsXiaobo Xia, Tongliang Liu, Bo Han 0003, Mingming Gong, Jun Yu, Gang Niu 0001, Masashi Sugiyama. iclr 2022: [doi]

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and MomentumZeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama. icml 2022: 24430-24459 [doi]

Adversarial Attack and Defense for Non-Parametric Two-Sample TestsXilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli. icml 2022: 24743-24769 [doi]

Towards Adversarially Robust Deep Image DenoisingHanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan. IJCAI 2022: 1516-1522 [doi]

Rethinking Class-Prior Estimation for Positive-Unlabeled LearningYu Yao, Tongliang Liu, Bo Han 0003, Mingming Gong, Gang Niu 0001, Masashi Sugiyama, Dacheng Tao. iclr 2022: [doi]

Exploiting Class Activation Value for Partial-Label LearningFei Zhang, Lei Feng, Bo Han 0003, Tongliang Liu, Gang Niu 0001, Tao Qin, Masashi Sugiyama. iclr 2022: [doi]

NoiLin: Improving adversarial training and correcting stereotype of noisy labelsJingfeng Zhang, Xilie Xu, Bo Han 0003, Tongliang Liu, LiZhen Cui, Gang Niu 0001, Masashi Sugiyama. tmlr, 2022, 2022. [doi]

2021

Classification with Rejection Based on Cost-sensitive ClassificationNontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama. icml 2021: 1507-1517 [doi]

Learning from Noisy Similar and Dissimilar DataSoham Dan, Han Bao 0002, Masashi Sugiyama. pkdd 2021: 233-249 [doi]

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence EstimatorMasahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama. aistats 2021: 1783-1791 [doi]

Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled LearningAlon Jacovi, Gang Niu 0001, Yoav Goldberg, Masashi Sugiyama. eacl 2021: 581-592 [doi]

Mixture Proportion Estimation in Weakly Supervised LearningMasashi Sugiyama. CIKM 2021: [doi]

Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentationTakeshi Teshima, Masashi Sugiyama. uai 2021: 86-96 [doi]

A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat MinimaZeke Xie, Issei Sato, Masashi Sugiyama. iclr 2021: [doi]

Mediated Uncoupled Learning: Learning Functions without Direct Input-output CorrespondencesIkko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama. icml 2021: 11637-11647 [doi]

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