| 2026 | AAAI | Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer. | Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu |
| 2025 | AAAI | Action-Agnostic Point-Level Supervision for Temporal Action Detection. | Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama |
| 2025 | AISTATS | Domain Adaptation and Entanglement: an Optimal Transport Perspective. | Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama |
| 2025 | AISTATS | Multi-Player Approaches for Dueling Bandits. | Or Raveh, Junya Honda, Masashi Sugiyama |
| 2025 | COLT | The Adaptive Complexity of Finding a Stationary Point. | Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama |
| 2025 | ICCV | Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. | Jie Xu, Na Zhao, Gang Niu, Masashi Sugiyama, Xiaofeng Zhu |
| 2025 | ICLR | Sharpness-Aware Black-Box Optimization. | Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang |
| 2025 | ICLR | Realistic Evaluation of Deep Partial-Label Learning Algorithms. | Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama |
| 2025 | ICLR | Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. | Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu |
| 2025 | ICLR | 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, Masashi Sugiyama, Yang Yu |
| 2025 | ICLR | Towards Effective Evaluations and Comparisons for LLM Unlearning Methods. | Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama |
| 2025 | ICLR | The adaptive complexity of parallelized log-concave sampling. | Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama |
| 2025 | ICML | Adaptive Localization of Knowledge Negation for Continual LLM Unlearning. | Abudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang |
| 2025 | ICML | Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability. | Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama |
| 2025 | ICML | Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models. | Huanjian Zhou, Masashi Sugiyama |
| 2025 | IJCAI | Label Distribution Learning with Biased Annotations Assisted by Multi-Label Learning. | Zhiqiang Kou, Si Qin, Hailin Wang, Jing Wang, Ming-Kun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng |
| 2024 | AAAI | The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. | Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama |
| 2024 | AAAI | Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. | Shintaro Nakamura, Masashi Sugiyama |
| 2024 | AISTATS | VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. | Guillaume Braun, Masashi Sugiyama |
| 2024 | AISTATS | Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. | Shintaro Nakamura, Masashi Sugiyama |
| 2024 | ECCV | Direct Distillation Between Different Domains. | Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama |
| 2024 | ECCV | Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. | Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2024 | EMNLP | Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. | Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama |
| 2024 | ICLR | Robust Similarity Learning with Difference Alignment Regularization. | Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama |
| 2024 | ICLR | Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. | Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj |
| 2024 | ICLR | Accurate Forgetting for Heterogeneous Federated Continual Learning. | Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama |
| 2024 | ICML | A General Framework for Learning from Weak Supervision. | Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj |
| 2024 | ICML | Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. | Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang |
| 2024 | ICML | Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. | Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou |
| 2024 | ICML | Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. | Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama |
| 2024 | ICML | Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. | Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2024 | ICML | Balancing Similarity and Complementarity for Federated Learning. | Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang |
| 2024 | ICML | 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, Masashi Sugiyama |
| 2024 | ICRA | An offline learning of behavior correction policy for vision-based robotic manipulation. | Qingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama |
| 2024 | WACV | Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data. | Yuki Tanaka, Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama |
| 2023 | ACML | Thompson Exploration with Best Challenger Rule in Best Arm Identification. | Jongyeong Lee, Junya Honda, Masashi Sugiyama |
| 2023 | ICASSP | Audio Signal Enhancement with Learning from Positive and Unlabeled Data. | Nobutaka Ito, Masashi Sugiyama |
| 2023 | ICCV | Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. | Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong |
| 2023 | ICCV | Multi-Label Knowledge Distillation. | Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang |
| 2023 | ICLR | 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, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama |
| 2023 | ICLR | Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. | Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou |
| 2023 | ICML | Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. | Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han |
| 2023 | ICML | GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. | Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon |
| 2023 | ICML | Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. | Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama |
| 2023 | ICML | A Category-theoretical Meta-analysis of Definitions of Disentanglement. | Yivan Zhang, Masashi Sugiyama |
| 2022 | ACML | Robust computation of optimal transport by β-potential regularization. | Shintaro Nakamura, Han Bao, Masashi Sugiyama |
| 2022 | ACML | Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization. | Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama |
| 2022 | AISTATS | Pairwise Supervision Can Provably Elicit a Decision Boundary. | Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama |
| 2022 | AISTATS | Predictive variational Bayesian inference as risk-seeking optimization. | Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama |
| 2022 | CIKM | Learning and Mining with Noisy Labels. | Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu |
| 2022 | CVPR | Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. | De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama |
| 2022 | ICLR | Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. | Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama |
| 2022 | ICLR | Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. | Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama |
| 2022 | ICLR | Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. | Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama |
| 2022 | ICLR | Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. | Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao |
| 2022 | ICLR | Exploiting Class Activation Value for Partial-Label Learning. | Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama |
| 2022 | ICML | To Smooth or Not? When Label Smoothing Meets Noisy Labels. | Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu |
| 2022 | ICML | Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. | Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama |
| 2022 | ICML | Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. | Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli |
| 2022 | IJCAI | Towards Adversarially Robust Deep Image Denoising. | Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan |
| 2021 | AISTATS | Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. | Han Bao, Masashi Sugiyama |
| 2021 | AISTATS | γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. | Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama |
| 2021 | AISTATS | A unified view of likelihood ratio and reparameterization gradients. | Paavo Parmas, Masashi Sugiyama |
| 2021 | AISTATS | Robust Imitation Learning from Noisy Demonstrations. | Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama |
| 2021 | CIKM | Mixture Proportion Estimation in Weakly Supervised Learning. | Masashi Sugiyama |
| 2021 | CVPR | On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. | Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama |
| 2021 | EACL | Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. | Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama |
| 2021 | ICLR | A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. | Zeke Xie, Issei Sato, Masashi Sugiyama |
| 2021 | ICLR | Geometry-aware Instance-reweighted Adversarial Training. | Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli |
| 2021 | ICML | Large-Margin Contrastive Learning with Distance Polarization Regularizer. | Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama |
| 2021 | ICML | Confidence Scores Make Instance-dependent Label-noise Learning Possible. | Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama |
| 2021 | ICML | Learning from Similarity-Confidence Data. | Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama |
| 2021 | ICML | Classification with Rejection Based on Cost-sensitive Classification. | Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama |
| 2021 | ICML | Learning Diverse-Structured Networks for Adversarial Robustness. | Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama |
| 2021 | ICML | Pointwise Binary Classification with Pairwise Confidence Comparisons. | Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama |
| 2021 | ICML | Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. | Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama |
| 2021 | ICML | Provably End-to-end Label-noise Learning without Anchor Points. | Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama |
| 2021 | ICML | Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. | Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama |
| 2021 | ICML | Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. | Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama |
| 2021 | ICML | Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. | Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama |
| 2021 | ICML | CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. | Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama |
| 2021 | ICML | Lower-Bounded Proper Losses for Weakly Supervised Classification. | Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama |
| 2021 | ICML | Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. | Yivan Zhang, Gang Niu, Masashi Sugiyama |
| 2021 | UAI | Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. | Takeshi Teshima, Masashi Sugiyama |
| 2020 | ACML | A One-step Approach to Covariate Shift Adaptation. | Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama |
| 2020 | AISTATS | Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. | Han Bao, Masashi Sugiyama |
| 2020 | AISTATS | Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. | Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama |
| 2020 | COLT | Calibrated Surrogate Losses for Adversarially Robust Classification. | Han Bao, Clayton Scott, Masashi Sugiyama |
| 2020 | ICML | SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. | Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama |
| 2020 | ICML | Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. | Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama |
| 2020 | ICML | Learning with Multiple Complementary Labels. | Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama |
| 2020 | ICML | Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. | Futoshi Futami, Issei Sato, Masashi Sugiyama |
| 2020 | ICML | Do We Need Zero Training Loss After Achieving Zero Training Error? | Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama |
| 2020 | ICML | Online Dense Subgraph Discovery via Blurred-Graph Feedback. | Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama |
| 2020 | ICML | Progressive Identification of True Labels for Partial-Label Learning. | Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama |
| 2020 | ICML | Variational Imitation Learning with Diverse-quality Demonstrations. | Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama |
| 2020 | ICML | Few-shot Domain Adaptation by Causal Mechanism Transfer. | Takeshi Teshima, Issei Sato, Masashi Sugiyama |
| 2020 | ICML | Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. | Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama |
| 2020 | ICML | Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. | Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli |
| 2020 | IJCAI | Binary Classification from Positive Data with Skewed Confidence. | Kazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama |
| 2020 | IROS | Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. | Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama |
| 2020 | MICCAI | Are Registration Uncertainty and Error Monotonically Associated? | Jie Luo, Sarah F. Frisken, Duo Wang, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III |
| 2020 | MICCAI | Calibrated Surrogate Maximization of Dice. | Marcus Nordstrm, Han Bao, Fredrik Lfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama |
| 2020 | WACV | Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes. | Masato Ishii, Takashi Takenouchi, Masashi Sugiyama |
| 2019 | AAAI | Bayesian Posterior Approximation via Greedy Particle Optimization. | Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama |
| 2019 | AAAI | Bzier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization. | Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama |
| 2019 | AAAI | Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. | Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama |
| 2019 | AAAI | Clipped Matrix Completion: A Remedy for Ceiling Effects. | Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama |
| 2019 | AAAI | Dueling Bandits with Qualitative Feedback. | Liyuan Xu, Junya Honda, Masashi Sugiyama |
| 2019 | ACML | Zero-shot Domain Adaptation Based on Attribute Information. | Masato Ishii, Takashi Takenouchi, Masashi Sugiyama |
| 2019 | EMNLP | Learning Only from Relevant Keywords and Unlabeled Documents. | Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama |
| 2019 | ICASSP | Binary Classification Only from Unlabeled Data by Iterative Unlabeled-unlabeled Classification. | Hirotaka Kaji, Masashi Sugiyama |
| 2019 | ICASSP | Learning Efficient Tensor Representations with Ring-structured Networks. | Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki |
| 2019 | ICLR | On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. | Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama |
| 2019 | ICLR | Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. | Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama |
| 2019 | ICML | On Symmetric Losses for Learning from Corrupted Labels. | Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama |
| 2019 | ICML | Classification from Positive, Unlabeled and Biased Negative Data. | Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama |
| 2019 | ICML | Complementary-Label Learning for Arbitrary Losses and Models. | Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama |
| 2019 | ICML | Imitation Learning from Imperfect Demonstration. | Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama |
| 2019 | ICML | How does Disagreement Help Generalization against Label Corruption? | Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama |
| 2019 | MICCAI | On the Applicability of Registration Uncertainty. | Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III, Sarah F. Frisken |
| 2019 | SDM | Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. | Nontawat Charoenphakdee, Masashi Sugiyama |
| 2018 | AAAI | Hierarchical Policy Search via Return-Weighted Density Estimation. | Takayuki Osa, Masashi Sugiyama |
| 2018 | AISTATS | Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. | Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama |
| 2018 | AISTATS | Variational Inference based on Robust Divergences. | Futoshi Futami, Issei Sato, Masashi Sugiyama |
| 2018 | AISTATS | A fully adaptive algorithm for pure exploration in linear bandits. | Liyuan Xu, Junya Honda, Masashi Sugiyama |
| 2018 | ICASSP | Multi Task Learning with Positive and Unlabeled Data and its Application to Mental State Prediction. | Hirotaka Kaji, Hayato Yamaguchi, Masashi Sugiyama |
| 2018 | ICLR | Guide Actor-Critic for Continuous Control. | Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama |
| 2018 | ICLR | Learning Efficient Tensor Representations with Ring Structure Networks. | Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki |
| 2018 | ICML | Classification from Pairwise Similarity and Unlabeled Data. | Han Bao, Gang Niu, Masashi Sugiyama |
| 2018 | ICML | Does Distributionally Robust Supervised Learning Give Robust Classifiers? | Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama |
| 2018 | ICML | Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. | Hideaki Imamura, Issei Sato, Masashi Sugiyama |
| 2018 | KDD | Active Feature Acquisition with Supervised Matrix Completion. | Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen |
| 2018 | MICCAI | A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation. | Jie Luo, Matthew Toews, Ins Machado, Sarah F. Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III |
| 2018 | UAI | Variational Inference for Gaussian Processes with Panel Count Data. | Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama |
| 2017 | AAAI | Policy Search with High-Dimensional Context Variables. | Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama |
| 2017 | ACML | Whitening-Free Least-Squares Non-Gaussian Component Analysis. | Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama |
| 2017 | AISTATS | Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. | Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama |
| 2017 | AISTATS | Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. | Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama |
| 2017 | ICML | Learning Discrete Representations via Information Maximizing Self-Augmented Training. | Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama |
| 2017 | ICML | Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. | Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2016 | ACML | Geometry-aware stationary subspace analysis. | Inbal Horev, Florian Yger, Masashi Sugiyama |
| 2016 | ACML | Multitask Principal Component Analysis. | Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama |
| 2016 | AISTATS | Non-Gaussian Component Analysis with Log-Density Gradient Estimation. | Hiroaki Sasaki, Gang Niu, Masashi Sugiyama |
| 2016 | ICML | Structure Learning of Partitioned Markov Networks. | Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu |
| 2016 | ICONIP | Modal Regression via Direct Log-Density Derivative Estimation. | Hiroaki Sasaki, Yurina Ono, Masashi Sugiyama |
| 2016 | UAI | Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. | Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama |
| 2015 | AAAI | Support Consistency of Direct Sparse-Change Learning in Markov Networks. | Song Liu, Taiji Suzuki, Masashi Sugiyama |
| 2015 | ACML | Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices. | Inbal Horev, Florian Yger, Masashi Sugiyama |
| 2015 | ACML | Continuous Target Shift Adaptation in Supervised Learning. | Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Masashi Sugiyama |
| 2015 | ACML | Class-prior Estimation for Learning from Positive and Unlabeled Data. | Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2015 | ACML | Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. | Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama |
| 2015 | ACML | Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. | Tingting Zhao, Gang Niu, Ning Xie, Jucheng Yang, Masashi Sugiyama |
| 2015 | AISTATS | Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. | Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama |
| 2015 | ICML | Convex Formulation for Learning from Positive and Unlabeled Data. | Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama |
| 2015 | IJCAI | Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning. | Ning Xie, Tingting Zhao, Feng Tian, Xiaohua Zhang, Masashi Sugiyama |
| 2015 | IROS | A dependence maximization approach towards street map-based localization. | Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono |
| 2015 | KDD | Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. | Yukino Baba, Hisashi Kashima, Yasunobu Nohara, Eiko Kai, Partha Pratim Ghosh, Rafiqul Islam Maruf, Ashir Ahmed, Masahiro Kuroda, Sozo Inoue, Tatsuo Hiramatsu, Michio Kimura, Shuji Shimizu, Kunihisa Kobayashi, Koji Tsuda, Masashi Sugiyama, Mathieu Blondel, Naonori Ueda, Masaru Kitsuregawa, Naoki Nakashima |
| 2014 | AISTATS | Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? | Shinichi Nakajima, Masashi Sugiyama |
| 2014 | AISTATS | Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. | Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee |
| 2014 | ICML | Transductive Learning with Multi-class Volume Approximation. | Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama |
| 2014 | ICML | Outlier Path: A Homotopy Algorithm for Robust SVM. | Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi |
| 2013 | ICML | Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. | Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama |
| 2013 | ICML | Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. | Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama |
| 2012 | ICASSP | Computationally efficient multi-label classification by least-squares probabilistic classifier. | Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama |
| 2012 | ICML | Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. | Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama |
| 2012 | ICML | Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching. | Marthinus Christoffel du Plessis, Masashi Sugiyama |
| 2012 | ICML | Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. | Ning Xie, Hirotaka Hachiya, Masashi Sugiyama |
| 2012 | ICPR | Designing various component analysis at will. | Akisato Kimura, Hitoshi Sakano, Hirokazu Kameoka, Masashi Sugiyama |
| 2012 | SSPR | Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation. | Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama |
| 2011 | AAAI | Trajectory Regression on Road Networks. | Tsuyoshi Id, Masashi Sugiyama |
| 2011 | AAAI | Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis. | Makoto Yamada, Masashi Sugiyama |
| 2011 | ICASSP | Automatic audio tag classification via semi-supervised canonical density estimation. | Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masashi Sugiyama, Makoto Yamada, Hirokazu Kameoka |
| 2011 | ICML | On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. | Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan |
| 2011 | ICML | On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution. | Masashi Sugiyama, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya |
| 2010 | AAAI | Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise. | Makoto Yamada, Masashi Sugiyama |
| 2010 | ICASSP | Automatic audio tagging using covariate shift adaptation. | Gordon Wichern, Makoto Yamada, Harvey D. Thornburg, Masashi Sugiyama, Andreas Spanias |
| 2010 | ICASSP | Direct importance estimation with probabilistic principal component analyzers. | Makoto Yamada, Masashi Sugiyama, Gordon Wichern |
| 2010 | ICASSP | Acceleration of sequence kernel computation for real-time speaker identification. | Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Tomoko Matsui |
| 2010 | ICML | Nonparametric Return Distribution Approximation for Reinforcement Learning. | Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka |
| 2010 | ICML | Implicit Regularization in Variational Bayesian Matrix Factorization. | Shinichi Nakajima, Masashi Sugiyama |
| 2010 | ICML | A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. | Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima |
| 2010 | ICPR | SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. | Akisato Kimura, Hirokazu Kameoka, Masashi Sugiyama, Takuho Nakano, Eisaku Maeda, Hitoshi Sakano, Katsuhiko Ishiguro |
| 2010 | ICPR | Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation. | Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara |
| 2010 | UAI | Parametric Return Density Estimation for Reinforcement Learning. | Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka |
| 2010 | SDM | Direct Density Ratio Estimation with Dimensionality Reduction. | Masashi Sugiyama, Satoshi Hara, Paul von Bnau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe |
| 2009 | ACML | Density Ratio Estimation: A New Versatile Tool for Machine Learning. | Masashi Sugiyama |
| 2009 | ICASSP | Covariate shift adaptation for semi-supervised speaker identification. | Makoto Yamada, Masashi Sugiyama, Tomoko Matsui |
| 2009 | IJCAI | Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. | Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama |
| 2009 | IJCNN | Probabilistic principal component analysis based on JoyStick Probability Selector. | Marko V. Jankovic, Masashi Sugiyama |
| 2009 | ICRA | Least absolute policy iteration for robust value function approximation. | Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura |
| 2009 | IDA | Estimating Squared-Loss Mutual Information for Independent Component Analysis. | Taiji Suzuki, Masashi Sugiyama |
| 2009 | ISIT | Mutual information approximation via maximum likelihood estimation of density ratio. | Taiji Suzuki, Masashi Sugiyama, Toshiyuki Tanaka |
| 2009 | PAKDD | Analysis of Variational Bayesian Matrix Factorization. | Shinichi Nakajima, Masashi Sugiyama |
| 2009 | SDM | Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. | Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, Koji Tsuda |
| 2009 | SDM | Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. | Yoshinobu Kawahara, Masashi Sugiyama |
| 2008 | AAAI | Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. | Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters |
| 2008 | COLT | On the Margin Explanation of Boosting Algorithms. | Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng |
| 2008 | ICDM | Inlier-Based Outlier Detection via Direct Density Ratio Estimation. | Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori |
| 2008 | ICML | Untitled record | Akiko Takeda, Masashi Sugiyama |
| 2008 | PAKDD | Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. | Masashi Sugiyama, Tsuyoshi Id, Shinichi Nakajima, Jun Sese |
| 2008 | SDM | Integration of Multiple Networks for Robust Label Propagation. | Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama |
| 2008 | SDM | Active Learning with Model Selection in Linear Regression. | Masashi Sugiyama, Neil Rubens |
| 2008 | SDM | Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. | Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama |
| 2007 | ICML | Asymptotic Bayesian generalization error when training and test distributions are different. | Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Mller |
| 2007 | ICRA | Value Function Approximation on Non-Linear Manifolds for Robot Motor Control. | Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar |
| 2007 | RecSys | Influence-based collaborative active learning. | Neil Rubens, Masashi Sugiyama |
| 2006 | ICASSP | Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis. | Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Vladimir G. Spokoiny, Klaus-Robert Mller |
| 2006 | ICML | Local Fisher discriminant analysis for supervised dimensionality reduction. | Masashi Sugiyama |
| 2006 | SSPR | Model Selection Using a Class of Kernels with an Invariant Metric. | Akira Tanaka, Masashi Sugiyama, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi |
| 2005 | ICANN | Model Selection Under Covariate Shift. | Masashi Sugiyama, Klaus-Robert Mller |
| 2004 | ESANN | Regularizing generalization error estimators: a novel approach to robust model selection. | Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Mller |
| 2002 | ICANN | Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces. | Masashi Sugiyama, Klaus-Robert Mller |
| 2000 | ESANN | A new information criterion for the selection of subspace models. | Masashi Sugiyama, Hidemitsu Ogawa |
| 2000 | IJCNN | Incremental Active Learning with Bias Reduction. | Masashi Sugiyama, Hidemitsu Ogawa |