対象会議のFull paperを全分野・掲載済み全期間でまとめています。1996～2025年の追加分には、氏名の一致・表記の正規化等に基づき同一人物と推定した対応を含みます。本人確認済みの対応や出版時所属の確認とは区別しています。表示する大学は著者ページの識別用で、過去論文の出版時所属や現在所属を示しません。追加分は大学ランキングやEdition別の件数に含みません。保存済みの1996～2025年の対象資料から確認できた範囲を追加しており、全業績の網羅を保証するものではありません。
2026 · AAAI
Robust Learning from Noisily Labeled Long-Tailed Data via Fairness RegularizerJiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
AI (General)
東京大学
2026 · ICLR
Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning AlgorithmsWei Wang, Dong-Dong Wu, Ming Li, Jingxiong Zhang, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Bilateral Information-aware Test-time Adaptation for Vision-Language ModelsJingwei Sun, Jianing ZHU, Jiangchao Yao, Gang Niu, Masashi Sugiyama, Bo Han
Machine Learning
東京大学
2026 · ICLR
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Vision-Language ModelRuijiang Dong, Zesheng Ye, Jianzhong Qi, Lei Feng, Feng Liu, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Decoupling the Class Label and the Target Concept in Machine UnlearningJianing ZHU, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Learning from Label Proportions via Proportional Value ClassificationTianhao Ma, Wei Wang, Ximing Li, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Practical estimation of the optimal classification error with soft labels and calibrationRyota Ushio, Takashi Ishida, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Rethinking Consistent Multi-Label Classification Under Inexact SupervisionWei Wang, Tianhao Ma, Ming-Kun Xie, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Towards Scalable Oversight via Partitioned Human SupervisionRen Yin, Takashi Ishida, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Towards Understanding Valuable Preference Data for Large Language Model AlignmentZizhuo Zhang, Qizhou Wang, Shanshan Ye, Jianing ZHU, Jiangchao Yao, Bo Han, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Unlocking the Power of Co-Occurrence in CLIP: A DualPrompt-Driven Method for Training-Free Zero-Shot Multi-Label ClassificationMing-Kun Xie, Zhiqiang Kou, Zhongnian Li, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICLR
Weak-to-Strong Diffusion with ReflectionLichen Bai, Masashi Sugiyama, Zeke Xie
Machine Learning
東京大学
2026 · ICML
Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-TuningBing Wang, Ximing Li, Changchun Li, Jinjin Chi, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICML
Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable RewardsJohannes Ackermann, Michael Noukhovitch, Takashi Ishida, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICML
Incorporating Importance Weighting in Optimal Transport Based Domain AlignmentOkan Koç, Alexander Soen, Shanglin Li, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICML
Positive-Unlabeled Learning with Extreme Scarcity of Labeled PositivesYuanchao Dai, Ximing Li, Wei Wang, Changchun Li, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2026 · ICML
Positive–Unlabeled Reinforcement Learning Distillation for On-Premise Small ModelsZhiqiang Kou, Junyang Chen, Xin-Qiang Cai, Xiaobo Xia, Ming-Kun Xie, Dong-Dong Wu, Biao Liu, Yuheng Jia, Xin Geng, Masashi Sugiyama, Tat-Seng Chua
Machine Learning
東京大学
2026 · ICML
Proteo-R1: Reasoning Foundation Models for De Novo Protein DesignFang Wu, Weihao Xuan, Heli Qi, Hanqun CAO, Heng-Jui Chang, Zeqi Zhou, Li Erran Li, Haokai Zhao, Jian Ma, Zijian Ma, Yu-Chi Cheng, Kuan Pang, Robert Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng Ann Heng, Naoto Yokoya, Masashi Sugiyama, Jure Leskovec, Yejin Choi
Machine Learning
東京大学 / 早稲田大学
2025 · AAAI
Action-Agnostic Point-Level Supervision for Temporal Action DetectionShuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama
AI (General)
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
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
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
東京大学
2025 · ICLR
Sharpness-Aware Black-Box OptimizationFeiyang YE, YUEMING LYU, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor Tsang
Machine Learning
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
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
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
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
東京大学
2025 · ICML
Parallel Simulation for Log-concave Sampling and Score-based Diffusion ModelsHuanjian Zhou, Masashi Sugiyama
Machine Learning
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)
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
東京大学
2025 · NeurIPS
The Adaptive Complexity of Minimizing Relative Fisher InformationHuanjian Zhou, Masashi Sugiyama
Machine Learning
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)
東京大学
2024 · AAAI
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed BanditShintaro Nakamura, Masashi Sugiyama
AI (General)
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
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
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
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
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
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
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
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
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
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
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
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
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
東京大学
2024 · ICRA
An offline learning of behavior correction policy for vision-based robotic manipulationQingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama
Robotics
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)
著者の全期間ページへの追加分（出版時所属は示していません）
2024 · NeurIPS
Enriching Disentanglement: From Logical Definitions to Quantitative MetricsYivan Zhang, Masashi Sugiyama
Machine Learning
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
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
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
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
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
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
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
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
東京大学
2023 · ICML
A Category-theoretical Meta-analysis of Definitions of DisentanglementYivan Zhang, Masashi Sugiyama
Machine Learning
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
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
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
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
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
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
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
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
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
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
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
東京大学
2023 · NeurIPS
Imitation Learning from Vague FeedbackXin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama
Machine Learning
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 [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
東京大学
2023 · NeurIPS
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation CostYu-Jie Zhang, Masashi Sugiyama
Machine Learning
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
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
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
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
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
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 [高知工科大学] [新潟大学], Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2022 · ICML
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and MomentumZeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
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
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
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)
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
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
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
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
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
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
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 [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
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
東京大学
2021 · ICML
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set ClassificationNan Lu, Shida Lei, Gang Niu, Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
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
東京大学
2021 · ICML
Classification with Rejection Based on Cost-sensitive ClassificationNontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama
Machine Learning
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
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
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
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
東京大学
2021 · ICML
Learning from Similarity-Confidence DataYuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
Machine Learning
東京大学
2021 · ICML
Lower-Bounded Proper Losses for Weakly Supervised ClassificationShuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama
Machine Learning
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
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
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
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
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
東京大学
2021 · NeurIPS
Loss function based second-order Jensen inequality and its application to particle variational inferenceFutoshi Futami [東京大学] [大阪大学], Tomoharu Iwata, naonori ueda, Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
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
東京大学
2020 · COLT
Calibrated Surrogate Losses for Adversarially Robust ClassificationHan Bao [京都大学] [東京大学] [東北大学], Clay Scott, Masashi Sugiyama
Machine Learning
著者の全期間ページへの追加分（出版時所属は示していません）
2020 · ICML
Accelerating the diffusion-based ensemble sampling by non-reversible dynamicsFutoshi Futami [東京大学] [大阪大学], Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
著者の全期間ページへの追加分（出版時所属は示していません）
2020 · ICML
Attacks Which Do Not Kill Training Make Adversarial Learning StrongerJingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli
Machine Learning
著者の全期間ページへの追加分（出版時所属は示していません）
2020 · ICML
Do We Need Zero Training Loss After Achieving Zero Training Error?Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
Machine Learning
著者の全期間ページへの追加分（出版時所属は示していません）
2020 · ICML
Few-shot Domain Adaptation by Causal Mechanism TransferTakeshi Teshima, Issei Sato, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2020 · ICML
Learning with Multiple Complementary LabelsLei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2020 · ICML
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian AnalysisYusuke Tsuzuku, Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
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2020 · ICML
Online Dense Subgraph Discovery via Blurred-Graph FeedbackYuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
Machine Learning
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2020 · ICML
Progressive Identification of True Labels for Partial-Label LearningJiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2020 · ICML
SIGUA: Forgetting May Make Learning with Noisy Labels More RobustBo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama
Machine Learning
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2020 · ICML
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary LabelsYu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
Machine Learning
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2020 · ICML
Variational Imitation Learning with Diverse-quality DemonstrationsVoot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2020 · IJCAI
Binary Classification from Positive Data with Skewed ConfidenceKazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama
AI (General)
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2020 · IROS
Simultaneous Planning for Item Picking and Placing by Deep Reinforcement LearningTatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama
Robotics
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2020 · NeurIPS
Analysis and Design of Thompson Sampling for Stochastic Partial MonitoringTaira Tsuchiya, Junya Honda, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2020 · NeurIPS
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism ApproximatorsTakeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama
Machine Learning
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2020 · NeurIPS
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise LearningYu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama
Machine Learning
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2020 · NeurIPS
Learning from Aggregate ObservationsYivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama
Machine Learning
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2020 · NeurIPS
Part-dependent Label Noise: Towards Instance-dependent Label NoiseXiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
Machine Learning
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2020 · NeurIPS
Provably Consistent Partial-Label LearningLei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
Machine Learning
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2020 · NeurIPS
Rethinking Importance Weighting for Deep Learning under Distribution ShiftTongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
Machine Learning
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2019 · AAAI
Bayesian Posterior Approximation via Greedy Particle OptimizationFutoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama
AI (General)
著者ページの大学: 東京大学
2019 · AAAI
Bézier Simplex Fitting: Describing Pareto Fronts of´ Simplicial Problems with Small Samples in Multi-Objective OptimizationKen Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama
AI (General)
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2019 · AAAI
Clipped Matrix Completion: A Remedy for Ceiling EffectsTakeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama
AI (General)
著者ページの大学: 東京大学
2019 · AAAI
Dueling Bandits with Qualitative FeedbackLiyuan Xu, Junya Honda, Masashi Sugiyama
AI (General)
著者ページの大学: 東京大学
2019 · AAAI
Unsupervised Domain Adaptation Based on Source-Guided DiscrepancySeiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
AI (General)
著者ページの大学: 東京大学
2019 · EMNLP
Learning Only from Relevant Keywords and Unlabeled DocumentsNontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama
Natural Language Processing
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2019 · ICLR
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationTakayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Machine Learning
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2019 · ICLR
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled DataNan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
Machine Learning
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2019 · ICML
Classification from Positive, Unlabeled and Biased Negative DataYu-Guan Hsieh, Gang Niu, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2019 · ICML
Complementary-Label Learning for Arbitrary Losses and ModelsTakashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama
Machine Learning
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2019 · ICML
How does Disagreement Help Generalization against Label Corruption?Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, Masashi Sugiyama
Machine Learning
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2019 · ICML
Imitation Learning from Imperfect DemonstrationYueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2019 · ICML
On Symmetric Losses for Learning from Corrupted LabelsNontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2019 · NeurIPS
Are Anchor Points Really Indispensable in Label-Noise Learning?Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama
Machine Learning
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2019 · NeurIPS
On the Calibration of Multiclass Classification with RejectionChenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2019 · NeurIPS
Uncoupled Regression from Pairwise Comparison DataLiyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
Machine Learning
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2018 · AAAI
Hierarchical Policy Search via Return-Weighted Density EstimationTakayuki Osa, Masashi Sugiyama
AI (General)
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2018 · ICML
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering ModelHideaki Imamura, Issei Sato, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2018 · ICML
Classification from Pairwise Similarity and Unlabeled DataHan Bao [京都大学] [東京大学] [東北大学], Gang Niu, Masashi Sugiyama
Machine Learning
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2018 · ICML
Does Distributionally Robust Supervised Learning Give Robust Classifiers?Weihua Hu, Gang Niu, Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama
Machine Learning
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2018 · KDD
Active Feature Acquisition with Supervised Matrix CompletionSheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen
Web, Data Mining & Information Retrieval
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2018 · NeurIPS
Binary Classification from Positive-Confidence DataTakashi Ishida, Gang Niu, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2018 · NeurIPS
Co-teaching: Robust training of deep neural networks with extremely noisy labelsBo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama
Machine Learning
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2018 · NeurIPS
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert SpacesMotoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Machine Learning
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2018 · NeurIPS
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural NetworksYusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2018 · NeurIPS
Masking: A New Perspective of Noisy SupervisionBo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, Masashi Sugiyama
Machine Learning
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2018 · NeurIPS
Uplift Modeling from Separate LabelsIkko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2017 · AAAI
Policy Search with High-Dimensional Context VariablesVoot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
AI (General)
著者の全期間ページへの追加分（出版時所属は示していません）
2017 · ICML
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingWeihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2017 · ICML
Semi-Supervised Classification Based on Classification from Positive and Unlabeled DataTomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama
Machine Learning
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2017 · NeurIPS
Expectation Propagation for t-Exponential Family Using q-AlgebraFutoshi Futami, Issei Sato, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2017 · NeurIPS
Generative Local Metric Learning for Kernel RegressionYung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D Lee
Machine Learning
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2017 · NeurIPS
Learning from Complementary LabelsTakashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama
Machine Learning
著者ページの大学: 東京大学
2017 · NeurIPS
Positive-Unlabeled Learning with Non-Negative Risk EstimatorRyuichi Kiryo, Gang Niu, Marthinus C du Plessis, Masashi Sugiyama
Machine Learning
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2016 · ICML
Structure Learning of Partitioned Markov NetworksSong Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu
Machine Learning
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2016 · NeurIPS
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative LearningGang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
Machine Learning
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2015 · AAAI
Support Consistency of Direct Sparse-Change Learning in Markov NetworksSong Liu, Taiji Suzuki, Masashi Sugiyama
AI (General)
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2015 · ICML
Convex Formulation for Learning from Positive and Unlabeled DataMarthinus Du Plessis, Gang Niu, Masashi Sugiyama
Machine Learning
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2015 · IROS
A dependence maximization approach towards street map-based localizationKiyoshi Irie, Masashi Sugiyama, Masahiro Tomono
Robotics
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2014 · ICML
Outlier Path: A Homotopy Algorithm for Robust SVMShinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi [名古屋工業大学] [名古屋大学]
Machine Learning
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2014 · ICML
Transductive Learning with Multi-class Volume ApproximationGang Niu, Bo Dai, Christoffel Plessis, Masashi Sugiyama
Machine Learning
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2014 · NeurIPS
Analysis of Learning from Positive and Unlabeled DataMarthinus C. du Plessis, Gang Niu, Masashi Sugiyama
Machine Learning
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2014 · NeurIPS
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAPShinichi Nakajima, Issei Sato [東京科学大学] [東京大学], Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
Machine Learning
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2014 · NeurIPS
Multitask learning meets tensor factorization: task imputation via convex optimizationKishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
Machine Learning
著者ページの大学: 東京大学
2013 · ICML
Infinitesimal Annealing for Training Semi-Supervised Support Vector MachinesKohei Ogawa, Motoki Imamura, Ichiro Takeuchi [名古屋工業大学] [名古屋大学], Masashi Sugiyama
Machine Learning
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2013 · ICML
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised LearningGang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama
Machine Learning
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2013 · NeurIPS
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace ClusteringShinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi [名古屋工業大学] [名古屋大学]
Machine Learning
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2013 · NeurIPS
Parametric Task LearningIchiro Takeuchi [名古屋工業大学] [名古屋大学], Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
Machine Learning
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2012 · ICML
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting.Ning Xie 0003, Hirotaka Hachiya, Masashi Sugiyama
Machine Learning
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2012 · ICML
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization.Gang Niu 0001, Bo Dai 0001, Makoto Yamada [京都大学] [沖縄科学技術大学院大学], Masashi Sugiyama
Machine Learning
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2012 · ICML
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching.Marthinus Christoffel du Plessis, Masashi Sugiyama
Machine Learning
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2012 · NeurIPS
Density-Difference EstimationMasashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus D. Plessis, Song Liu, Ichiro Takeuchi [名古屋工業大学] [名古屋大学]
Machine Learning
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2012 · NeurIPS
Perfect Dimensionality Recovery by Variational Bayesian PCAShinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. D. Babacan
Machine Learning
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2011 · AAAI
Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis.Makoto Yamada [京都大学] [沖縄科学技術大学院大学], Masashi Sugiyama
AI (General)
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2011 · AAAI
Trajectory Regression on Road Networks.Tsuyoshi Idé, Masashi Sugiyama
AI (General)
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2011 · NeurIPS
Analysis and Improvement of Policy Gradient EstimationTingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
Machine Learning
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2011 · NeurIPS
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise IndependentShinichi Nakajima, Masashi Sugiyama, S. D. Babacan
Machine Learning
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2011 · NeurIPS
Relative Density-Ratio Estimation for Robust Distribution ComparisonMakoto Yamada [京都大学] [沖縄科学技術大学院大学], Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
Machine Learning
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2011 · NeurIPS
Target Neighbor Consistent Feature Weighting for Nearest Neighbor ClassificationIchiro Takeuchi, Masashi Sugiyama
Machine Learning
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2010 · AAAI
Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian NoiseMakoto Yamada [京都大学] [沖縄科学技術大学院大学], Masashi Sugiyama
AI (General)
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2010 · ICML
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices.Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima
Machine Learning
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2010 · ICML
Implicit Regularization in Variational Bayesian Matrix Factorization.Shinichi Nakajima, Masashi Sugiyama
Machine Learning
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2010 · ICML
Nonparametric Return Distribution Approximation for Reinforcement Learning.Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka 0003
Machine Learning
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2010 · NeurIPS
Global Analytic Solution for Variational Bayesian Matrix FactorizationShinichi Nakajima, Masashi Sugiyama, Ryota Tomioka
Machine Learning
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2009 · ICRA
Least absolute policy iteration for robust value function approximation.Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura
Robotics
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2009 · IJCAI
Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement LearningTakayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama
AI (General)
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2008 · AAAI
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation.Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters 0001
AI (General)
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2008 · COLT
On the Margin Explanation of Boosting Algorithms.Liwei Wang 0001, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng
Machine Learning
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2008 · ICML
ν-Support Vector Machine as Conditional Value-at-Risk MinimizationAkiko Takeda, Masashi Sugiyama
Machine Learning
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2008 · NeurIPS
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier DetectionTakafumi Kanamori, Shohei Hido, Masashi Sugiyama
Machine Learning
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2007 · ICML
Asymptotic Bayesian generalization error when training and test distributions are different.Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller
Machine Learning
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2007 · ICRA
Value Function Approximation on Non-Linear Manifolds for Robot Motor Control.Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar
Robotics
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2007 · NeurIPS
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift AdaptationMasashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V. Buenau, Motoaki Kawanabe
Machine Learning
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2007 · NeurIPS
Multi-Task Learning via Conic ProgrammingTsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
Machine Learning
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2006 · ICML
Local Fisher discriminant analysis for supervised dimensionality reduction.Masashi Sugiyama
Machine Learning
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2006 · NeurIPS
Mixture Regression for Covariate ShiftMasashi Sugiyama, Amos J. Storkey
Machine Learning
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2005 · NeurIPS
Active Learning for Misspecified ModelsMasashi Sugiyama
Machine Learning
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2005 · NeurIPS
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension ReductionGilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir Spokoiny, Klaus-Robert Müller
Machine Learning
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1999 · NeurIPS
Training Data Selection for Optimal Generalization in Trigonometric Polynomial NetworksMasashi Sugiyama, Hidemitsu Ogawa
Machine Learning
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