Masashi Sugiyama

808 papers A* 158A 46B 9C 21Misc 10Journal 482Unranked 74

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Are Multimodal Large Language Models Good Annotators for Image Tagging? CoRR Ming-Kun Xie, Jiahao Xiao, Zhiqiang Kou, Zhongnian Li, Gang Niu, Masashi Sugiyama
2026	J	`jnl`	Atom-Motif Contrastive Transformer for Molecular Property Prediction. ACM Trans. Intell. Syst. Technol. Wentao Yu, Shuo Chen, Chen Gong, Bo Han, Gang Niu, Masashi Sugiyama
2026	J	`jnl`	Bifrost: Steering Strategic Trajectories to Bridge Contextual Gaps for Self-Improving Agents. CoRR Quan M. Tran, Zhuo Huang, Wenbin Zhang, Bo Han, Koji Yatani, Masashi Sugiyama, Tongliang Liu
2026	J	`jnl`	BrokenBind: Universal Modality Exploration beyond Dataset Boundaries. CoRR Zhuo Huang, Runnan Chen, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu
2026	J	`jnl`	Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship. CoRR Nang Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang, Masashi Sugiyama
2026	J	`jnl`	Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship. Trans. Mach. Learn. Res. Nang Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang, Masashi Sugiyama
2026	J	`jnl`	Estimating Expected Calibration Error for Positive-Unlabeled Learning. Trans. Mach. Learn. Res. Ryuichi Kiryo, Futoshi Futami, Masashi Sugiyama
2026	J	`jnl`	Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards. CoRR Johannes Ackermann, Michael Noukhovitch, Takashi Ishida, Masashi Sugiyama
2026	J	`jnl`	HapticMatch: An Exploration for Generative Material Haptic Simulation and Interaction. CoRR Mingxin Zhang, Yu Yao, Yasutoshi Makino, Hiroyuki Shinoda, Masashi Sugiyama
2026	J	`jnl`	Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models. CoRR Zhiqiang 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
2026	J	`jnl`	Riemannian Langevin Dynamics: Strong Convergence of Geometric Euler-Maruyama Scheme. CoRR Zhiyuan Zhan, Masashi Sugiyama
2026	A*	`conf`	Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer. AAAI Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
2026	J	`jnl`	Understanding Guidance Scale in Diffusion Models from a Geometric Perspective. Trans. Mach. Learn. Res. Zhiyuan Zhan, Liuzhuozheng Li, Masashi Sugiyama
2026	J	`jnl`	VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction. CoRR Xin-Qiang Cai, Masashi Sugiyama
2026	J	`jnl`	Weakly Supervised Classification with Pre-Trained Models: A Robust Fine-Tuning Approach. Mach. Learn. Ming Li, Wei Wang, Masashi Sugiyama
2025	J	`jnl`	A Fast Algorithm for the Real-Valued Combinatorial Pure Exploration of the Multi-Armed Bandit. Neural Comput. Shintaro Nakamura, Masashi Sugiyama
2025	J	`jnl`	Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms. CoRR Wei Wang, Dong-Dong Wu, Ming Li, Jingxiong Zhang, Gang Niu, Masashi Sugiyama
2025	J	`jnl`	Accurate Forgetting for Heterogeneous Federated Continual Learning. CoRR Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama
2025	A*	`conf`	Action-Agnostic Point-Level Supervision for Temporal Action Detection. AAAI Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama
2025	A*	`conf`	Adaptive Localization of Knowledge Negation for Continual LLM Unlearning. ICML Abudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2025	J	`jnl`	AlphaApollo: Orchestrating Foundation Models and Professional Tools into a Self-Evolving System for Deep Agentic Reasoning. CoRR Zhanke Zhou, Chentao Cao, Xiao Feng, Xuan Li, Zongze Li, Xiangyu Lu, Jiangchao Yao, Weikai Huang, Linrui Xu, Tian Cheng, Guanyu Jiang, Yiming Zheng, Brando Miranda, Tongliang Liu, Sanmi Koyejo, Masashi Sugiyama, Bo Han
2025	A	`conf`	Domain Adaptation and Entanglement: an Optimal Transport Perspective. AISTATS Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama
2025	J	`jnl`	Domain Adaptation and Entanglement: an Optimal Transport Perspective. CoRR Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama
2025	J	`jnl`	Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. Wenshui Luo, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong
2025	J	`jnl`	From Coefficients to Directions: Rethinking Model Merging with Directional Alignment. CoRR Zhikang Chen, Sen Cui, Deheng Ye, Min Zhang, Gang Niu, Yu Zhang, Masashi Sugiyama, Tingting Zhu
2025	J	`jnl`	Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update. CoRR Yu-Jie Zhang, Sheng-An Xu, Peng Zhao, Masashi Sugiyama
2025	J	`jnl`	Impact of Noisy Supervision in Foundation Model Learning. IEEE Trans. Pattern Anal. Mach. Intell. Hao Chen, Zihan Wang, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj, Jindong Wang
2025	J	`jnl`	Importance Weighting for Aligning Language Models under Deployment Distribution Shift. Trans. Mach. Learn. Res. Thanawat Lodkaew, Tongtong Fang, Takashi Ishida, Masashi Sugiyama
2025	J	`jnl`	LLM Routing with Dueling Feedback. CoRR Chao-Kai Chiang, Takashi Ishida, Masashi Sugiyama
2025	A*	`conf`	Label Distribution Learning with Biased Annotations Assisted by Multi-Label Learning. IJCAI Zhiqiang Kou, Si Qin, Hailin Wang, Jing Wang, Ming-Kun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng
2025	J	`jnl`	Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation. CoRR Zhiqiang Kou, Si Qin, Hailin Wang, Ming-Kun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng
2025	J	`jnl`	Learning Robust Diffusion Models from Imprecise Supervision. CoRR Dong-Dong Wu, Jiacheng Cui, Wei Wang, Zhiqiang Shen, Masashi Sugiyama
2025	A*	`conf`	Learning View-invariant World Models for Visual Robotic Manipulation. ICLR Jing-Cheng Pang, Nan Tang, Kaiyuan Li, Yuting Tang, Xin-Qiang Cai, Zhen-Yu Zhang, Gang Niu, Masashi Sugiyama, Yang Yu
2025	A	`conf`	Multi-Player Approaches for Dueling Bandits. AISTATS Or Raveh, Junya Honda, Masashi Sugiyama
2025	A*	`conf`	Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability. ICML Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama
2025	J	`jnl`	Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability. CoRR Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama
2025	J	`jnl`	Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback. CoRR Johannes Ackermann, Takashi Ishida, Masashi Sugiyama
2025	J	`jnl`	On Symmetric Losses for Robust Policy Optimization with Noisy Preferences. CoRR Soichiro Nishimori, Yu-Jie Zhang, Thanawat Lodkaew, Masashi Sugiyama
2025	A*	`conf`	Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models. ICML Huanjian Zhou, Masashi Sugiyama
2025	J	`jnl`	Practical estimation of the optimal classification error with soft labels and calibration. CoRR Ryota Ushio, Takashi Ishida, Masashi Sugiyama
2025	A*	`conf`	Realistic Evaluation of Deep Partial-Label Learning Algorithms. ICLR Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2025	J	`jnl`	Realistic Evaluation of Deep Partial-Label Learning Algorithms. CoRR Wei Wang, Dong-Dong Wu, Jindong Wang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2025	J	`jnl`	Recursive Reward Aggregation. CoRR Yuting Tang, Yivan Zhang, Johannes Ackermann, Yu-Jie Zhang, Soichiro Nishimori, Masashi Sugiyama
2025	J	`jnl`	Reinforcement Learning from Bagged Reward. Trans. Mach. Learn. Res. Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama
2025	J	`jnl`	Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers. CoRR Xin-Qiang Cai, Wei Wang, Feng Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama
2025	J	`jnl`	Rethinking Consistent Multi-Label Classification under Inexact Supervision. CoRR Wei Wang, Tianhao Ma, Ming-Kun Xie, Gang Niu, Masashi Sugiyama
2025	J	`jnl`	Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective. CoRR Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai, Ming-Kun Xie, Biao Liu, Changwei Wang, Lei Feng, Yuheng Jia, Gang Niu, Masashi Sugiyama, Xin Geng
2025	J	`jnl`	Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. CoRR Jie Xu, Na Zhao, Gang Niu, Masashi Sugiyama, Xiaofeng Zhu
2025	J	`jnl`	Safety at Scale: A Comprehensive Survey of Large Model Safety. CoRR Xingjun Ma, Yifeng Gao, Yixu Wang, Ruofan Wang, Xin Wang, Ye Sun, Yifan Ding, Hengyuan Xu, Yunhao Chen, Yunhan Zhao, Hanxun Huang, Yige Li, Jiaming Zhang, Xiang Zheng, Yang Bai, Zuxuan Wu, Xipeng Qiu, Jingfeng Zhang, Yiming Li, Jun Sun, Cong Wang, Jindong Gu, Baoyuan Wu, Siheng Chen, Tianwei Zhang, Yang Liu, Mingming Gong, Tongliang Liu, Shirui Pan, Cihang Xie, Tianyu Pang, Yinpeng Dong, Ruoxi Jia, Yang Zhang, Shiqing Ma, Xiangyu Zhang, Neil Gong, Chaowei Xiao, Sarah M. Erfani, Bo Li, Masashi Sugiyama, Dacheng Tao, James Bailey, Yu-Gang Jiang
2025	J	`jnl`	Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety. Found. Trends Priv. Secur. Xingjun Ma, Yifeng Gao, Yixu Wang, Ruofan Wang, Xin Wang, Ye Sun, Yifan Ding, Hengyuan Xu, Yunhao Chen, Yunhao Zhao, Hanxun Huang, Yige Li, Yutao Wu, Jiaming Zhang, Xiang Zheng, Yang Bai, Yiming Li, Zuxuan Wu, Xipeng Qiu, Jingfeng Zhang, Xudong Han, Haonan Li, Jun Sun, Cong Wang, Jindong Gu, Baoyuan Wu, Siheng Chen, Tianwei Zhang, Yang Liu, Mingming Gong, Tongliang Liu, Shirui Pan, Cihang Xie, Tianyu Pang, Yinpeng Dong, Ruoxi Jia, Yang Zhang, Shiqing Ma, Xiangyu Zhang, Neil Gong, Chaowei Xiao, Sarah M. Erfani, Tim Baldwin, Bo Li, Masashi Sugiyama, Dacheng Tao, James Bailey, Yu-Gang Jiang
2025	J	`jnl`	Scalable Oversight via Partitioned Human Supervision. CoRR Ren Yin, Takashi Ishida, Masashi Sugiyama
2025	A*	`conf`	Sharpness-Aware Black-Box Optimization. ICLR Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang
2025	A*	`conf`	The Adaptive Complexity of Finding a Stationary Point. COLT Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama
2025	J	`jnl`	The Adaptive Complexity of Finding a Stationary Point. CoRR Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama
2025	A*	`conf`	The adaptive complexity of parallelized log-concave sampling. ICLR Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama
2025	A*	`conf`	Towards Effective Evaluations and Comparisons for LLM Unlearning Methods. ICLR Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama
2025	A*	`conf`	Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. ICLR Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu
2025	J	`jnl`	Towards Scalable Oversight with Collaborative Multi-Agent Debate in Error Detection. CoRR Yongqiang Chen, Gang Niu, James Cheng, Bo Han, Masashi Sugiyama
2025	J	`jnl`	Towards Understanding Valuable Preference Data for Large Language Model Alignment. CoRR Zizhuo Zhang, Qizhou Wang, Shanshan Ye, Jianing Zhu, Jiangchao Yao, Bo Han, Masashi Sugiyama
2025	J	`jnl`	Unified Risk Analysis for Weakly Supervised Learning. Trans. Mach. Learn. Res. Chao-Kai Chiang, Masashi Sugiyama
2025	J	`jnl`	Weak-to-Strong Diffusion with Reflection. CoRR Lichen Bai, Masashi Sugiyama, Zeke Xie
2025	J	`jnl`	What Is Preference Optimization Doing, How and Why? CoRR Yue Wang, Qizhou Wang, Zizhuo Zhang, Ang Li, Gang Niu, Bo Han, Masashi Sugiyama
2025	J	`jnl`	What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? CoRR Ming-Kun Xie, Jiahao Xiao, Gang Niu, Lei Feng, Zhiqiang Kou, Min-Ling Zhang, Masashi Sugiyama
2024	A*	`conf`	A General Framework for Learning from Weak Supervision. ICML Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
2024	J	`jnl`	A General Framework for Learning from Weak Supervision. CoRR Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
2024	A*	`conf`	Accurate Forgetting for Heterogeneous Federated Continual Learning. ICLR Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama
2024	J	`jnl`	Action-Agnostic Point-Level Supervision for Temporal Action Detection. CoRR Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama
2024	J	`jnl`	Adaptive complexity of log-concave sampling. CoRR Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama
2024	A*	`conf`	An offline learning of behavior correction policy for vision-based robotic manipulation. ICRA Qingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama
2024	A	`conf`	Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data. WACV Yuki Tanaka, Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama
2024	J	`jnl`	BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama
2024	A*	`conf`	Balancing Similarity and Complementarity for Federated Learning. ICML Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2024	J	`jnl`	Balancing Similarity and Complementarity for Federated Learning. CoRR Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2024	J	`jnl`	Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning. CoRR Yuting Tang, Xin-Qiang Cai, Jing-Cheng Pang, Qiyu Wu, Yao-Xiang Ding, Masashi Sugiyama
2024	J	`jnl`	Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. J. Medical Syst. Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto
2024	A*	`conf`	Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	J	`jnl`	Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	J	`jnl`	Decoupling the Class Label and the Target Concept in Machine Unlearning. CoRR Jianing Zhu, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama
2024	—	`conf`	Direct Distillation Between Different Domains. ECCV (80) Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama
2024	J	`jnl`	Direct Distillation between Different Domains. CoRR Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama
2024	J	`jnl`	Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	—	`conf`	Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. ECCV (52) Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024	A*	`conf`	Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. ICML Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou
2024	A*	`conf`	Enriching Disentanglement: From Logical Definitions to Quantitative Metrics. NeurIPS Yivan Zhang, Masashi Sugiyama
2024	A	`conf`	Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. AISTATS Shintaro Nakamura, Masashi Sugiyama
2024	J	`jnl`	Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. CoRR Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. ICML Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. NeurIPS Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xiang Li, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
2024	J	`jnl`	Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen, Ning Xie, Gang Niu, Masashi Sugiyama
2024	A*	`conf`	Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. ICML Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
2024	J	`jnl`	Learning with Noisy Foundation Models. CoRR Hao Chen, Jindong Wang, Zihan Wang, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj
2024	J	`jnl`	Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. CoRR Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama
2024	A*	`conf`	Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. ICML Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang
2024	J	`jnl`	Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. CoRR Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang
2024	J	`jnl`	Multi-Player Approaches for Dueling Bandits. CoRR Or Raveh, Junya Honda, Masashi Sugiyama
2024	J	`jnl`	Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. RLJ Johannes Ackermann, Takayuki Osa, Masashi Sugiyama
2024	J	`jnl`	Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. CoRR Johannes Ackermann, Takayuki Osa, Masashi Sugiyama
2024	J	`jnl`	On Unsupervised Prompt Learning for Classification with Black-box Language Models. CoRR Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024	J	`jnl`	On the Effectiveness of Adversarial Training Against Backdoor Attacks. IEEE Trans. Neural Networks Learn. Syst. Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
2024	J	`jnl`	On the Robustness of Average Losses for Partial-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama
2024	—	`conf`	Overcoming Continuous Distribution Shifts: Challenges in Online Machine Learning. BCI Masashi Sugiyama
2024	J	`jnl`	Parallel simulation for sampling under isoperimetry and score-based diffusion models. CoRR Huanjian Zhou, Masashi Sugiyama
2024	J	`jnl`	Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution. CoRR Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama
2024	J	`jnl`	Reinforcement Learning with Options and State Representation. CoRR Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric
2024	A*	`conf`	Robust Similarity Learning with Difference Alignment Regularization. ICLR Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama
2024	J	`jnl`	Sharpness-Aware Black-Box Optimization. CoRR Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang
2024	A*	`conf`	Slight Corruption in Pre-training Data Makes Better Diffusion Models. NeurIPS Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj
2024	J	`jnl`	Slight Corruption in Pre-training Data Makes Better Diffusion Models. CoRR Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj
2024	A*	`conf`	Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment. NeurIPS Zhen-Yu Zhang, Zhiyu Xie, Huaxiu Yao, Masashi Sugiyama
2024	A*	`conf`	The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. AAAI Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama
2024	J	`jnl`	The Survival Bandit Problem. Trans. Mach. Learn. Res. Charles Riou, Junya Honda, Masashi Sugiyama
2024	A*	`conf`	Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. AAAI Shintaro Nakamura, Masashi Sugiyama
2024	A*	`conf`	Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. ICLR Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj
2024	J	`jnl`	Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama
2024	A	`conf`	VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. AISTATS Guillaume Braun, Masashi Sugiyama
2024	J	`jnl`	VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. CoRR Guillaume Braun, Masashi Sugiyama
2024	A*	`conf`	Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. EMNLP Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama
2024	J	`jnl`	Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. CoRR Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama
2024	A*	`conf`	What Makes Partial-Label Learning Algorithms Effective? NeurIPS Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng
2023	A*	`conf`	A Category-theoretical Meta-analysis of Definitions of Disentanglement. ICML Yivan Zhang, Masashi Sugiyama
2023	J	`jnl`	A Category-theoretical Meta-analysis of Definitions of Disentanglement. CoRR Yivan Zhang, Masashi Sugiyama
2023	A*	`conf`	Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. NeurIPS Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama
2023	J	`jnl`	Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. CoRR Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama
2023	J	`jnl`	Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model. CoRR Sora Satake, Yoshihiro Nagano, Masashi Sugiyama, Masahiro Fujiwara, Yasutoshi Makino, Hiroyuki Shinoda
2023	J	`jnl`	Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. CoRR Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama
2023	J	`jnl`	Atom-Motif Contrastive Transformer for Molecular Property Prediction. CoRR Wentao Yu, Shuo Chen, Chen Gong, Gang Niu, Masashi Sugiyama
2023	Misc	`conf`	Audio Signal Enhancement with Learning from Positive and Unlabeled Data. ICASSP Nobutaka Ito, Masashi Sugiyama
2023	J	`jnl`	BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama
2023	A*	`conf`	Binary Classification with Confidence Difference. NeurIPS Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2023	J	`jnl`	Binary Classification with Confidence Difference. CoRR Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama
2023	J	`jnl`	Boundary-restricted metric learning. Mach. Learn. Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	A*	`conf`	Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	J	`jnl`	Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. Chen Gong, Yongliang Ding, Bo Han, Gang Niu, Jian Yang, Jane You, Dacheng Tao, Masashi Sugiyama
2023	J	`jnl`	Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class. CoRR Shintaro Nakamura, Masashi Sugiyama
2023	A*	`conf`	Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. ICCV Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
2023	J	`jnl`	Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong
2023	A*	`conf`	Distributional Pareto-Optimal Multi-Objective Reinforcement Learning. NeurIPS Xin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama, Ashley Llorens
2023	A*	`conf`	Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han
2023	J	`jnl`	Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han
2023	A*	`conf`	Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
2023	J	`jnl`	Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
2023	A*	`conf`	Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. NeurIPS Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
2023	J	`jnl`	Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. CoRR Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
2023	A*	`conf`	Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. NeurIPS Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
2023	J	`jnl`	Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. CoRR Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
2023	J	`jnl`	Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2023	J	`jnl`	Enriching Disentanglement: Definitions to Metrics. CoRR Yivan Zhang, Masashi Sugiyama
2023	J	`jnl`	Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
2023	J	`jnl`	Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR Shintaro Nakamura, Masashi Sugiyama
2023	A*	`conf`	GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. ICML Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon
2023	J	`jnl`	GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. CoRR Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon
2023	A*	`conf`	Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. NeurIPS Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2023	A*	`conf`	Imitation Learning from Vague Feedback. NeurIPS Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama
2023	J	`jnl`	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. CoRR Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
2023	A*	`conf`	Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. ICLR Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Learning Intention-Aware Policies in Deep Reinforcement Learning. Neural Comput. Tingting Zhao, S. Wu, G. Li, Y. Chen, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Learning With Proper Partial Labels. Neural Comput. Zhenguo Wu, Jiaqi Lv, Masashi Sugiyama
2023	J	`jnl`	Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu
2023	A*	`conf`	Multi-Label Knowledge Distillation. ICCV Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	J	`jnl`	Multi-Label Knowledge Distillation. CoRR Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2023	A*	`conf`	On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. NeurIPS Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama
2023	A*	`conf`	Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost. NeurIPS Yu-Jie Zhang, Masashi Sugiyama
2023	A*	`conf`	Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama
2023	J	`jnl`	Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. CoRR Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama
2023	J	`jnl`	Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation. Mach. Learn. Shota Nakajima, Masashi Sugiyama
2023	J	`jnl`	Representation learning for continuous action spaces is beneficial for efficient policy learning. Neural Networks Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama
2023	J	`jnl`	Root cause estimation of faults in production processes: a novel approach inspired by approximate Bayesian computation. Int. J. Prod. Res. Yosuke Otsubo, Naoya Otani, Megumi Chikasue, Mineyuki Nishino, Masashi Sugiyama
2023	A*	`conf`	Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. ICLR Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou
2023	C	`conf`	Thompson Exploration with Best Challenger Rule in Best Arm Identification. ACML Jongyeong Lee, Junya Honda, Masashi Sugiyama
2023	J	`jnl`	Thompson Exploration with Best Challenger Rule in Best Arm Identification. CoRR Jongyeong Lee, Junya Honda, Masashi Sugiyama
2023	J	`jnl`	Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR Shintaro Nakamura, Masashi Sugiyama
2023	J	`jnl`	Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. CoRR Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj
2023	J	`jnl`	Unified Risk Analysis for Weakly Supervised Learning. CoRR Chao-Kai Chiang, Masashi Sugiyama
2023	J	`jnl`	Universal Approximation Property of Invertible Neural Networks. J. Mach. Learn. Res. Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama
2022	A*	`conf`	Adapting to Online Label Shift with Provable Guarantees. NeurIPS Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou
2022	J	`jnl`	Adapting to Online Label Shift with Provable Guarantees. CoRR Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou
2022	A*	`conf`	Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. ICML Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
2022	A*	`conf`	Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. ICML Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
2022	J	`jnl`	Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. CoRR Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
2022	A*	`conf`	Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
2022	J	`jnl`	Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
2022	J	`jnl`	Audio Signal Enhancement with Learning from Positive and Unlabelled Data. CoRR Nobutaka Ito, Masashi Sugiyama
2022	J	`jnl`	Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. Chen Gong, Jian Yang, Jane You, Masashi Sugiyama
2022	J	`jnl`	Deep learning, reinforcement learning, and world models. Neural Networks Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto
2022	J	`jnl`	Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2022	J	`jnl`	Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. CoRR Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama
2022	J	`jnl`	Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
2022	A*	`conf`	Exploiting Class Activation Value for Partial-Label Learning. ICLR Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
2022	J	`jnl`	Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
2022	A*	`conf`	Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. ICLR Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
2022	A*	`conf`	Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. NeurIPS Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	Improving imbalanced classification using near-miss instances. Expert Syst. Appl. Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima
2022	A*	`conf`	Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama
2022	J	`jnl`	Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama
2022	J	`jnl`	Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Learning Contrastive Embedding in Low-Dimensional Space. NeurIPS Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama
2022	A	`conf`	Learning and Mining with Noisy Labels. CIKM Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu
2022	J	`jnl`	Learning from Multiple Unlabeled Datasets with Partial Risk Regularization. CoRR Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama
2022	J	`jnl`	Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Trans. Inf. Syst. Hiroki Ishiguro, Takashi Ishida, Masashi Sugiyama
2022	J	`jnl`	Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	LocalDrop: A Hybrid Regularization for Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama
2022	A*	`conf`	Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
2022	C	`conf`	Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization. ACML Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama
2022	J	`jnl`	Neural Networks special issue on Artificial Intelligence and Brain Science. Neural Networks Kenji Doya, Karl J. Friston, Masashi Sugiyama, Joshua B. Tenenbaum
2022	J	`jnl`	NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama
2022	J	`jnl`	On the Effectiveness of Adversarial Training against Backdoor Attacks. CoRR Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
2022	A	`conf`	Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
2022	A	`conf`	Predictive variational Bayesian inference as risk-seeking optimization. AISTATS Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
2022	J	`jnl`	Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning. CoRR Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
2022	C	`conf`	Robust computation of optimal transport by β-potential regularization. ACML Shintaro Nakamura, Han Bao, Masashi Sugiyama
2022	J	`jnl`	Robust computation of optimal transport by β-potential regularization. CoRR Shintaro Nakamura, Han Bao, Masashi Sugiyama
2022	A*	`conf`	Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
2022	A*	`conf`	Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. NeurIPS Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang
2022	J	`jnl`	The Survival Bandit Problem. CoRR Charles Riou, Junya Honda, Masashi Sugiyama
2022	A*	`conf`	To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
2022	A*	`conf`	Towards Adversarially Robust Deep Image Denoising. IJCAI Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan
2022	J	`jnl`	Towards Adversarially Robust Deep Image Denoising. CoRR Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan
2022	J	`jnl`	Universal approximation property of invertible neural networks. CoRR Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama
2021	A*	`conf`	A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. ICLR Zeke Xie, Issei Sato, Masashi Sugiyama
2021	J	`jnl`	A One-Step Approach to Covariate Shift Adaptation. SN Comput. Sci. Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
2021	J	`jnl`	A Symmetric Loss Perspective of Reliable Machine Learning. CoRR Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
2021	A	`conf`	A unified view of likelihood ratio and reparameterization gradients. AISTATS Paavo Parmas, Masashi Sugiyama
2021	J	`jnl`	A unified view of likelihood ratio and reparameterization gradients. CoRR Paavo Parmas, Masashi Sugiyama
2021	J	`jnl`	Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2021	J	`jnl`	Approximating Instance-Dependent Noise via Instance-Confidence Embedding. CoRR Yivan Zhang, Masashi Sugiyama
2021	J	`jnl`	Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. Neural Comput. Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama
2021	A*	`conf`	Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama
2021	J	`jnl`	Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama
2021	A*	`conf`	CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. ICML Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama
2021	J	`jnl`	CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. CoRR Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama
2021	J	`jnl`	Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
2021	A*	`conf`	Classification with Rejection Based on Cost-sensitive Classification. ICML Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama
2021	A*	`conf`	Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
2021	J	`jnl`	Constraint learning for control tasks with limited duration barrier functions. Autom. Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt
2021	J	`jnl`	Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2021	J	`jnl`	Discovering Diverse Solutions in Deep Reinforcement Learning. CoRR Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2021	A	`conf`	Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS Han Bao, Masashi Sugiyama
2021	A*	`conf`	Geometry-aware Instance-reweighted Adversarial Training. ICLR Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli
2021	J	`jnl`	Guided Interpolation for Adversarial Training. CoRR Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama
2021	J	`jnl`	Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. CoRR Takeshi Teshima, Masashi Sugiyama
2021	A	`conf`	Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. UAI Takeshi Teshima, Masashi Sugiyama
2021	J	`jnl`	Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Instance Correction for Learning with Open-set Noisy Labels. CoRR Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Large-Margin Contrastive Learning with Distance Polarization Regularizer. ICML Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama
2021	A*	`conf`	Learning Diverse-Structured Networks for Adversarial Robustness. ICML Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama
2021	J	`jnl`	Learning Diverse-Structured Networks for Adversarial Robustness. CoRR Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama
2021	A*	`conf`	Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML Yivan Zhang, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR Yivan Zhang, Gang Niu, Masashi Sugiyama
2021	—	`conf`	Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) Soham Dan, Han Bao, Masashi Sugiyama
2021	A*	`conf`	Learning from Similarity-Confidence Data. ICML Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Learning from Similarity-Confidence Data. CoRR Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Learning with Proper Partial Labels. CoRR Zhenguo Wu, Masashi Sugiyama
2021	J	`jnl`	LocalDrop: A Hybrid Regularization for Deep Neural Networks. CoRR Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama
2021	A*	`conf`	Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
2021	J	`jnl`	Loss function based second-order Jensen inequality and its application to particle variational inference. CoRR Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
2021	A*	`conf`	Lower-Bounded Proper Losses for Weakly Supervised Classification. ICML Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama
2021	J	`jnl`	Lower-bounded proper losses for weakly supervised classification. CoRR Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama
2021	A*	`conf`	Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. ICML Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama
2021	J	`jnl`	Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. CoRR Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama
2021	—	`conf`	Mixture Proportion Estimation in Weakly Supervised Learning. CIKM Workshops Masashi Sugiyama
2021	J	`jnl`	Multi-Class Classification from Single-Class Data with Confidences. CoRR Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama
2021	A*	`conf`	On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. CVPR Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama
2021	J	`jnl`	On the Robustness of Average Losses for Partial-Label Learning. CoRR Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama
2021	A*	`conf`	Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
2021	A*	`conf`	Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. ICML Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
2021	J	`jnl`	Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. CoRR Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
2021	J	`jnl`	Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation. CoRR Shota Nakajima, Masashi Sugiyama
2021	A*	`conf`	Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
2021	J	`jnl`	Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
2021	J	`jnl`	Provably End-to-end Label-Noise Learning without Anchor Points. CoRR Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
2021	A*	`conf`	Provably End-to-end Label-noise Learning without Anchor Points. ICML Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	Rethinking Importance Weighting for Transfer Learning. CoRR Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama
2021	A	`conf`	Robust Imitation Learning from Noisy Demonstrations. AISTATS Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
2021	J	`jnl`	Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama
2021	A	`conf`	Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama
2021	J	`jnl`	Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations. CoRR Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou
2021	J	`jnl`	Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2021	J	`jnl`	Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics. CoRR Masato Ishii, Masashi Sugiyama
2021	J	`jnl`	Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama
2021	A	`conf`	γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast. CoRR Zeke Xie, Issei Sato, Masashi Sugiyama
2020	C	`conf`	A One-step Approach to Covariate Shift Adaptation. ACML Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
2020	J	`jnl`	A One-step Approach to Covariate Shift Adaptation. CoRR Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
2020	J	`jnl`	A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama
2020	A*	`conf`	Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. ICML Futoshi Futami, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	Active deep Q-learning with demonstration. Mach. Learn. Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
2020	J	`jnl`	Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia. CoRR Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
2020	A*	`conf`	Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. NeurIPS Taira Tsuchiya, Junya Honda, Masashi Sugiyama
2020	J	`jnl`	Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. CoRR Taira Tsuchiya, Junya Honda, Masashi Sugiyama
2020	—	`conf`	Are Registration Uncertainty and Error Monotonically Associated? MICCAI (3) Jie Luo, Sarah F. Frisken, Duo Wang, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III
2020	J	`jnl`	Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. CoRR Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama
2020	A*	`conf`	Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. ICML Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli
2020	J	`jnl`	Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CoRR Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli
2020	A*	`conf`	Binary Classification from Positive Data with Skewed Confidence. IJCAI Kazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama
2020	J	`jnl`	Binary Classification from Positive Data with Skewed Confidence. CoRR Kazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama
2020	J	`jnl`	Binary classification with ambiguous training data. CoRR Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama
2020	J	`jnl`	Binary classification with ambiguous training data. Mach. Learn. Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama
2020	A*	`conf`	Calibrated Surrogate Losses for Adversarially Robust Classification. COLT Han Bao, Clayton Scott, Masashi Sugiyama
2020	J	`jnl`	Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR Han Bao, Clayton Scott, Masashi Sugiyama
2020	—	`conf`	Calibrated Surrogate Maximization of Dice. MICCAI (4) Marcus Nordström, Han Bao, Fredrik Löfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama
2020	A	`conf`	Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. AISTATS Han Bao, Masashi Sugiyama
2020	J	`jnl`	Classification from Triplet Comparison Data. Neural Comput. Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	Classification with Rejection Based on Cost-sensitive Classification. CoRR Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama
2020	J	`jnl`	Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation. CoRR Yuko Kuroki, Junya Honda, Masashi Sugiyama
2020	J	`jnl`	Confidence Scores Make Instance-dependent Label-noise Learning Possible. CoRR Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
2020	A*	`conf`	Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. NeurIPS Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama
2020	J	`jnl`	Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. CoRR Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama
2020	J	`jnl`	Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation? CoRR Jie Luo, Guangshen Ma, Sarah F. Frisken, Parikshit Juvekar, Nazim Haouchine, Zhe Xu, Yiming Xiao, Alexandra J. Golby, Patrick J. Codd, Masashi Sugiyama, William M. Wells III
2020	A*	`conf`	Do We Need Zero Training Loss After Achieving Zero Training Error? ICML Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	A*	`conf`	Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. NeurIPS Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. CoRR Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama
2020	A*	`conf`	Few-shot Domain Adaptation by Causal Mechanism Transfer. ICML Takeshi Teshima, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	Few-shot Domain Adaptation by Causal Mechanism Transfer. CoRR Takeshi Teshima, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent. CoRR Mehdi Abbana Bennani, Masashi Sugiyama
2020	J	`jnl`	Geometry-aware Instance-reweighted Adversarial Training. CoRR Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli
2020	J	`jnl`	LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning. CoRR Kei Mukaiyama, Issei Sato, Masashi Sugiyama
2020	A*	`conf`	Learning from Aggregate Observations. NeurIPS Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama
2020	J	`jnl`	Learning from Aggregate Observations. CoRR Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama
2020	J	`jnl`	Learning from Noisy Similar and Dissimilar Data. CoRR Soham Dan, Han Bao, Masashi Sugiyama
2020	A*	`conf`	Learning with Multiple Complementary Labels. ICML Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
2020	J	`jnl`	Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
2020	A	`conf`	Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. AISTATS Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama
2020	A*	`conf`	Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. ICML Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. CoRR Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama
2020	A*	`conf`	Online Dense Subgraph Discovery via Blurred-Graph Feedback. ICML Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
2020	J	`jnl`	Online Dense Subgraph Discovery via Blurred-Graph Feedback. CoRR Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
2020	A*	`conf`	Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
2020	A	`conf`	Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes. WACV Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
2020	J	`jnl`	Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
2020	J	`jnl`	Pointwise Binary Classification with Pairwise Confidence Comparisons. CoRR Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
2020	J	`jnl`	Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback. Neural Comput. Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
2020	J	`jnl`	Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. Mach. Learn. Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik
2020	C	`ed.`	Proceedings of The 12th Asian Conference on Machine Learning, ACML 2020, 18-20 November 2020, Bangkok, Thailand. ACML Sinno Jialin Pan, Masashi Sugiyama
2020	A*	`conf`	Progressive Identification of True Labels for Partial-Label Learning. ICML Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama
2020	J	`jnl`	Progressive Identification of True Labels for Partial-Label Learning. CoRR Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama
2020	A*	`conf`	Provably Consistent Partial-Label Learning. NeurIPS Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
2020	J	`jnl`	Provably Consistent Partial-Label Learning. CoRR Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
2020	A*	`conf`	Rethinking Importance Weighting for Deep Learning under Distribution Shift. NeurIPS Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Rethinking Importance Weighting for Deep Learning under Distribution Shift. CoRR Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Robust Imitation Learning from Noisy Demonstrations. CoRR Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
2020	A*	`conf`	SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama
2020	J	`jnl`	Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
2020	A	`conf`	Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. IROS Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama
2020	J	`jnl`	Stable Weight Decay Regularization. CoRR Zeke Xie, Issei Sato, Masashi Sugiyama
2020	J	`jnl`	Stochastic Multichannel Ranking with Brain Dynamics Preferences. Neural Comput. Yuangang Pan, Ivor W. Tsang, Avinash Kumar Singh, Chin-Teng Lin, Masashi Sugiyama
2020	J	`jnl`	Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. CoRR Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami, Issei Sato, Junya Honda, Masashi Sugiyama
2020	J	`jnl`	Towards Mixture Proportion Estimation without Irreducibility. CoRR Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
2020	A*	`conf`	Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. ICML Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2020	J	`jnl`	Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. CoRR Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
2020	J	`jnl`	Unsupervised key frame selection using information theory and colour histogram difference. Int. J. Bus. Intell. Data Min. Janya Sainui, Masashi Sugiyama
2020	A*	`conf`	Variational Imitation Learning with Diverse-quality Demonstrations. ICML Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
2019	J	`jnl`	A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme. CoRR Paavo Parmas, Masashi Sugiyama
2019	J	`jnl`	An analytic formulation for positive-unlabeled learning via weighted integral probability metric. CoRR Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik
2019	A*	`conf`	Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama
2019	A*	`conf`	Bayesian Posterior Approximation via Greedy Particle Optimization. AAAI Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama
2019	Misc	`conf`	Binary Classification Only from Unlabeled Data by Iterative Unlabeled-unlabeled Classification. ICASSP Hirotaka Kaji, Masashi Sugiyama
2019	J	`jnl`	Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama
2019	A*	`conf`	Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization. AAAI Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama
2019	J	`jnl`	Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. CoRR Han Bao, Masashi Sugiyama
2019	J	`jnl`	Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
2019	A*	`conf`	Classification from Positive, Unlabeled and Biased Negative Data. ICML Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Classification from Triplet Comparison Data. CoRR Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2019	A*	`conf`	Clipped Matrix Completion: A Remedy for Ceiling Effects. AAAI Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama
2019	A*	`conf`	Complementary-Label Learning for Arbitrary Losses and Models. ICML Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2019	J	`jnl`	Constraint Learning for Control Tasks with Limited Duration Barrier Functions. CoRR Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt
2019	J	`jnl`	Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2019	J	`jnl`	Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation. CoRR Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki, Masashi Sugiyama
2019	A*	`conf`	Dueling Bandits with Qualitative Feedback. AAAI Liyuan Xu, Junya Honda, Masashi Sugiyama
2019	J	`jnl`	ECG-Based Concentration Recognition With Multi-Task Regression. IEEE Trans. Biomed. Eng. Hirotaka Kaji, Hisashi Iizuka, Masashi Sugiyama
2019	J	`jnl`	Fast and Robust Rank Aggregation against Model Misspecification. CoRR Yuangang Pan, Weijie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
2019	J	`jnl`	Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). Mach. Learn. Masashi Sugiyama, Yung-Kyun Noh
2019	J	`jnl`	Good arm identification via bandit feedback. Mach. Learn. Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, Masashi Sugiyama
2019	—	`conf`	Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. ICLR (Poster) Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2019	J	`jnl`	Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. CoRR Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2019	A*	`conf`	How does Disagreement Help Generalization against Label Corruption? ICML Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
2019	J	`jnl`	How does Disagreement Help Generalization against Label Corruption? CoRR Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
2019	A*	`conf`	Imitation Learning from Imperfect Demonstration. ICML Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
2019	J	`jnl`	Imitation Learning from Imperfect Demonstration. CoRR Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
2019	Misc	`conf`	Learning Efficient Tensor Representations with Ring-structured Networks. ICASSP Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki
2019	—	`conf`	Learning Only from Relevant Keywords and Unlabeled Documents. EMNLP/IJCNLP (1) Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama
2019	J	`jnl`	Learning Only from Relevant Keywords and Unlabeled Documents. CoRR Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama
2019	J	`jnl`	Learning from Indirect Observations. CoRR Yivan Zhang, Nontawat Charoenphakdee, Masashi Sugiyama
2019	J	`jnl`	Millionaire: a hint-guided approach for crowdsourcing. Mach. Learn. Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama
2019	J	`jnl`	Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. CoRR Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	New Tricks for Estimating Gradients of Expectations. CoRR Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama
2019	J	`jnl`	Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis. CoRR Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
2019	J	`jnl`	On Possibility and Impossibility of Multiclass Classification with Rejection. CoRR Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
2019	A*	`conf`	On Symmetric Losses for Learning from Corrupted Labels. ICML Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
2019	J	`jnl`	On Symmetric Losses for Learning from Corrupted Labels. CoRR Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
2019	—	`conf`	On the Applicability of Registration Uncertainty. MICCAI (2) 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	A*	`conf`	On the Calibration of Multiclass Classification with Rejection. NeurIPS Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
2019	—	`conf`	On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. ICLR (Poster) Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2019	J	`jnl`	Online Multiclass Classification Based on Prediction Margin for Partial Feedback. CoRR Takuo Kaneko, Issei Sato, Masashi Sugiyama
2019	J	`jnl`	Pilot Study on Verifying the Monotonic Relationship between Error and Uncertainty in Deformable Registration for Neurosurgery. CoRR Jie Luo, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III, Sarah F. Frisken
2019	J	`jnl`	Polynomial-time Algorithms for Combinatorial Pure Exploration with Full-bandit Feedback. CoRR Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
2019	A	`conf`	Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. SDM Nontawat Charoenphakdee, Masashi Sugiyama
2019	J	`jnl`	Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics. CoRR Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama
2019	J	`jnl`	Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. CoRR Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama
2019	J	`jnl`	Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. CoRR Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama
2019	J	`jnl`	Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2019	J	`jnl`	Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge. CoRR Kenshin Abe, Zijian Xu, Issei Sato, Masashi Sugiyama
2019	A	`ed.`	The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan AISTATS Kamalika Chaudhuri, Masashi Sugiyama
2019	A*	`conf`	Uncoupled Regression from Pairwise Comparison Data. NeurIPS Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
2019	J	`jnl`	Uncoupled Regression from Pairwise Comparison Data. CoRR Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
2019	—	`ch.`	Unsupervised Discrete Representation Learning. Explainable AI Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama
2019	A*	`conf`	Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
2019	J	`jnl`	VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
2019	J	`jnl`	Where is the Bottleneck of Adversarial Learning with Unlabeled Data? CoRR Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama
2019	C	`conf`	Zero-shot Domain Adaptation Based on Attribute Information. ACML Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
2019	J	`jnl`	Zero-shot Domain Adaptation Based on Attribute Information. CoRR Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
2018	—	`conf`	A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation. MICCAI (4) Jie Luo, Matthew Toews, Inês 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	J	`jnl`	A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation. CoRR Jie Luo, Matthew Toews, Inês 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	A	`conf`	A fully adaptive algorithm for pure exploration in linear bandits. AISTATS Liyuan Xu, Junya Honda, Masashi Sugiyama
2018	J	`jnl`	Active Deep Q-learning with Demonstration. CoRR Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
2018	A*	`conf`	Active Feature Acquisition with Supervised Matrix Completion. KDD Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen
2018	J	`jnl`	Active Feature Acquisition with Supervised Matrix Completion. CoRR Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen
2018	J	`jnl`	Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data. CoRR Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama
2018	A*	`conf`	Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. ICML Hideaki Imamura, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. CoRR Hideaki Imamura, Issei Sato, Masashi Sugiyama
2018	A	`conf`	Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. AISTATS Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. Neural Comput. Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee
2018	A*	`conf`	Binary Classification from Positive-Confidence Data. NeurIPS Takashi Ishida, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Binary Matrix Completion Using Unobserved Entries. CoRR Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama
2018	A*	`conf`	Classification from Pairwise Similarity and Unlabeled Data. ICML Han Bao, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Classification from Pairwise Similarity and Unlabeled Data. CoRR Han Bao, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Classification from Positive, Unlabeled and Biased Negative Data. CoRR Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Clipped Matrix Completion: a Remedy for Ceiling Effects. CoRR Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Co-sampling: Training Robust Networks for Extremely Noisy Supervision. CoRR Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama
2018	A*	`conf`	Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama
2018	J	`jnl`	Complementary-Label Learning for Arbitrary Losses and Models. CoRR Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2018	A*	`conf`	Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. NeurIPS Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
2018	J	`jnl`	Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. CoRR Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
2018	J	`jnl`	Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2018	A*	`conf`	Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Dueling Bandits with Qualitative Feedback. CoRR Liyuan Xu, Junya Honda, Masashi Sugiyama
2018	J	`jnl`	Frank-Wolfe Stein Sampling. CoRR Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama
2018	—	`conf`	Guide Actor-Critic for Continuous Control. ICLR (Poster) Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama
2018	A*	`conf`	Hierarchical Policy Search via Return-Weighted Density Estimation. AAAI Takayuki Osa, Masashi Sugiyama
2018	—	`conf`	Learning Efficient Tensor Representations with Ring Structure Networks. ICLR (Workshop) Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki
2018	A*	`conf`	Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. NeurIPS Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. CoRR Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
2018	A*	`conf`	Masking: A New Perspective of Noisy Supervision. NeurIPS Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama
2018	J	`jnl`	Masking: A New Perspective of Noisy Supervision. CoRR Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama
2018	J	`jnl`	Matrix Co-completion for Multi-label Classification with Missing Features and Labels. CoRR Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama
2018	J	`jnl`	Millionaire: A Hint-guided Approach for Crowdsourcing. CoRR Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama
2018	Misc	`conf`	Multi Task Learning with Positive and Unlabeled Data and its Application to Mental State Prediction. ICASSP Hirotaka Kaji, Hayato Yamaguchi, Masashi Sugiyama
2018	J	`jnl`	On the Ambiguity of Registration Uncertainty. CoRR Jie Luo, Sarah F. Frisken, Karteek Popuri, Dana Cobzas, Frank Preiswerk, Matthew Toews, Miaomiao Zhang, Hongyi Ding, Polina Golland, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III
2018	J	`jnl`	On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. CoRR Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama
2018	J	`jnl`	Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. CoRR Nontawat Charoenphakdee, Masashi Sugiyama
2018	J	`jnl`	Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. CoRR Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama
2018	J	`jnl`	Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Stochastic Divergence Minimization for Biterm Topic Models. IEICE Trans. Inf. Syst. Zhenghang Cui, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. Neural Comput. Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
2018	A*	`conf`	Uplift Modeling from Separate Labels. NeurIPS Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama
2018	J	`jnl`	Using the variogram for vector outlier screening: application to feature-based image registration. Int. J. Comput. Assist. Radiol. Surg. Jie Luo, Sarah F. Frisken, Inês Machado, Miaomiao Zhang, Steve Pieper, Polina Golland, Matthew Toews, Prashin Unadkat, Alireza Sedghi, Haoyin Zhou, Alireza Mehrtash, Frank Preiswerk, Cheng-Chieh Cheng, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III
2018	A	`conf`	Variational Inference based on Robust Divergences. AISTATS Futoshi Futami, Issei Sato, Masashi Sugiyama
2018	A	`conf`	Variational Inference for Gaussian Processes with Panel Count Data. UAI Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama
2017	J	`jnl`	Binary Classification from Positive-Confidence Data. CoRR Takashi Ishida, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Class-prior estimation for learning from positive and unlabeled data. Mach. Learn. Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction. Neural Comput. Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
2017	A	`conf`	Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. AISTATS Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama
2017	J	`jnl`	Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. CoRR Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	—	`conf`	Expectation Propagation for t-Exponential Family Using q-Algebra. NIPS Futoshi Futami, Issei Sato, Masashi Sugiyama
2017	J	`jnl`	Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). Mach. Learn. Robert J. Durrant, Kee-Eung Kim, Geoffrey Holmes, Stephen Marsland, Masashi Sugiyama, Zhi-Hua Zhou
2017	—	`conf`	Generative Local Metric Learning for Kernel Regression. NIPS Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank C. Park, Daniel D. Lee
2017	J	`jnl`	Geometry-aware principal component analysis for symmetric positive definite matrices. Mach. Learn. Inbal Horev, Florian Yger, Masashi Sugiyama
2017	J	`jnl`	Hierarchical Policy Search via Return-Weighted Density Estimation. CoRR Takayuki Osa, Masashi Sugiyama
2017	J	`jnl`	Homotopy continuation approaches for robust SV classification and regression. Mach. Learn. Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi
2017	J	`jnl`	Introduction: special issue of selected papers from ACML 2015. Mach. Learn. Geoffrey Holmes, Tie-Yan Liu, Hang Li, Irwin King, Masashi Sugiyama, Zhi-Hua Zhou
2017	J	`jnl`	Learning Discrete Representations via Information Maximizing Self Augmented Training. CoRR Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama
2017	A*	`conf`	Learning Discrete Representations via Information Maximizing Self-Augmented Training. ICML Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama
2017	J	`jnl`	Learning Efficient Tensor Representations with Ring Structure Networks. CoRR Qibin Zhao, Masashi Sugiyama, Andrzej Cichocki
2017	—	`conf`	Learning from Complementary Labels. NIPS Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama
2017	J	`jnl`	Learning from Complementary Labels. CoRR Takashi Ishida, Gang Niu, Masashi Sugiyama
2017	A	`conf`	Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. AISTATS Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama
2017	—	`conf`	Lung lesion detection in FDG-PET/CT with Gaussian process regression. Computer-Aided Diagnosis Ryosuke Kamesawa, Issei Sato, Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto, Naoto Hayashi, Masashi Sugiyama
2017	J	`jnl`	Misdirected Registration Uncertainty. CoRR Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, William M. Wells III, Masashi Sugiyama
2017	J	`jnl`	Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
2017	A*	`conf`	Policy Search with High-Dimensional Context Variables. AAAI Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
2017	—	`conf`	Positive-Unlabeled Learning with Non-Negative Risk Estimator. NIPS Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama
2017	J	`jnl`	Positive-Unlabeled Learning with Non-Negative Risk Estimator. CoRR Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama
2017	J	`jnl`	Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
2017	J	`jnl`	Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning. CoRR Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	A*	`conf`	Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives. CoRR Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Namgil Lee, Ivan V. Oseledets, Masashi Sugiyama, Danilo P. Mandic
2017	J	`jnl`	Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. Found. Trends Mach. Learn. Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Namgil Lee, Ivan V. Oseledets, Masashi Sugiyama, Danilo P. Mandic
2017	C	`conf`	Whitening-Free Least-Squares Non-Gaussian Component Analysis. ACML Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
2016	—	`ed.`	Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain NIPS Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett
2016	J	`jnl`	An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions. Neural Comput. Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama
2016	J	`jnl`	Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. CoRR Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Class-prior Estimation for Learning from Positive and Unlabeled Data. CoRR Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Computationally Efficient Class-Prior Estimation under Class Balance Change Using Energy Distance. IEICE Trans. Inf. Syst. Hideko Kawakubo, Marthinus Christoffel du Plessis, Masashi Sugiyama
2016	J	`jnl`	Dependence maximization localization: a novel approach to 2D street-map-based robot localization. Adv. Robotics Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono
2016	J	`jnl`	Direct Density Derivative Estimation. Neural Comput. Hiroaki Sasaki, Yung-Kyun Noh, Gang Niu, Masashi Sugiyama
2016	A	`conf`	Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
2016	C	`conf`	Geometry-aware stationary subspace analysis. ACML Inbal Horev, Florian Yger, Masashi Sugiyama
2016	J	`jnl`	Geometry-aware stationary subspace analysis. CoRR Inbal Horev, Florian Yger, Masashi Sugiyama
2016	—	`conf`	Modal Regression via Direct Log-Density Derivative Estimation. ICONIP (2) Hiroaki Sasaki, Yurina Ono, Masashi Sugiyama
2016	J	`jnl`	Model-based reinforcement learning with dimension reduction. Neural Networks Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
2016	C	`conf`	Multitask Principal Component Analysis. ACML Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama
2016	A	`conf`	Non-Gaussian Component Analysis with Log-Density Gradient Estimation. AISTATS Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Policy Search with High-Dimensional Context Variables. CoRR Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
2016	J	`jnl`	Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation. Neural Comput. Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama
2016	J	`jnl`	Reinterpreting the Transformation Posterior in Probabilistic Image Registration. CoRR Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, Masashi Sugiyama
2016	J	`jnl`	Robust supervised learning under uncertainty in dataset shift. CoRR Weihua Hu, Issei Sato, Masashi Sugiyama
2016	—	`conf`	Semi-supervised sufficient dimension reduction under class-prior change. TAAI Hideko Kawakubo, Masashi Sugiyama
2016	A*	`conf`	Structure Learning of Partitioned Markov Networks. ICML Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu
2016	—	`conf`	Target-less camera-LiDAR extrinsic calibration using a bagged dependence estimator. CASE Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono
2016	J	`jnl`	Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. CoRR Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama
2016	—	`conf`	Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. NIPS Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
2016	J	`jnl`	Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. Neural Comput. Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama
2016	J	`jnl`	Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning. IEEE Robotics Autom. Mag. Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
2015	A	`conf`	A dependence maximization approach towards street map-based localization. IROS Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono
2015	—	`ch.`	Active Learning in Recommender Systems. Recommender Systems Handbook Neil Rubens, Mehdi Elahi, Masashi Sugiyama, Dain Kaplan
2015	—	`ed.`	Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada NIPS Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett
2015	—	`conf`	Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study. EUSIPCO Florian Yger, Fabien Lotte, Masashi Sugiyama
2015	J	`jnl`	Bandit-Based Task Assignment for Heterogeneous Crowdsourcing. CoRR Hao Zhang, Yao Ma, Masashi Sugiyama
2015	J	`jnl`	Bandit-Based Task Assignment for Heterogeneous Crowdsourcing. Neural Comput. Hao Zhang, Yao Ma, Masashi Sugiyama
2015	C	`conf`	Class-prior Estimation for Learning from Positive and Unlabeled Data. ACML Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2015	J	`jnl`	Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
2015	J	`jnl`	Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. Neural Comput. Voot Tangkaratt, Ning Xie, Masashi Sugiyama
2015	C	`conf`	Continuous Target Shift Adaptation in Supervised Learning. ACML Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Masashi Sugiyama
2015	J	`jnl`	Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
2015	A*	`conf`	Convex Formulation for Learning from Positive and Unlabeled Data. ICML Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2015	J	`jnl`	Cross-Domain Matching with Squared-Loss Mutual Information. IEEE Trans. Pattern Anal. Mach. Intell. Makoto Yamada, Leonid Sigal, Michalis Raptis, Machiko Toyoda, Yi Chang, Masashi Sugiyama
2015	J	`jnl`	Direct Density Ratio Estimation with Convolutional Neural Networks with Application in Outlier Detection. IEICE Trans. Inf. Syst. Hyun Ha Nam, Masashi Sugiyama
2015	A	`conf`	Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. AISTATS Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama
2015	J	`jnl`	Direct conditional probability density estimation with sparse feature selection. Mach. Learn. Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
2015	C	`conf`	Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices. ACML Inbal Horev, Florian Yger, Masashi Sugiyama
2015	J	`jnl`	Homotopy Continuation Approaches for Robust SV Classification and Regression. CoRR Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi
2015	J	`jnl`	Importance-weighted covariance estimation for robust common spatial pattern. Pattern Recognit. Lett. Alessandro Balzi, Florian Yger, Masashi Sugiyama
2015	J	`jnl`	Introduction: special issue of selected papers of ACML 2013. Mach. Learn. Cheng Soon Ong, Wray L. Buntine, Tu Bao Ho, Masashi Sugiyama, Geoffrey I. Webb
2015	—	`conf`	Minimum dependency key frames selection via quadratic mutual information. ICDIM Janya Sainui, Masashi Sugiyama
2015	J	`jnl`	Online Direct Density-Ratio Estimation Applied to Inlier-Based Outlier Detection. Neural Comput. Marthinus Christoffel du Plessis, Hiroaki Shiino, Masashi Sugiyama
2015	J	`jnl`	Online Markov decision processes with policy iteration. CoRR Yao Ma, Hao Zhang, Masashi Sugiyama
2015	A*	`conf`	Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. KDD 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
2015	C	`conf`	Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. ACML Tingting Zhao, Gang Niu, Ning Xie, JuCheng Yang, Masashi Sugiyama
2015	—	`book`	Statistical Reinforcement Learning - Modern Machine Learning Approaches. Masashi Sugiyama
2015	A*	`conf`	Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning. IJCAI Ning Xie, Tingting Zhao, Feng Tian, Xiaohua Zhang, Masashi Sugiyama
2015	C	`conf`	Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. ACML Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama
2015	J	`jnl`	Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices. CoRR Florian Yger, Masashi Sugiyama
2015	A*	`conf`	Support Consistency of Direct Sparse-Change Learning in Markov Networks. AAAI Song Liu, Taiji Suzuki, Masashi Sugiyama
2015	J	`jnl`	Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing. CoRR Hao Zhang, Masashi Sugiyama
2015	—	`conf`	Task selection for bandit-based task assignment in heterogeneous crowdsourcing. TAAI Hao Zhang, Masashi Sugiyama
2015	J	`jnl`	Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. CoRR Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama
2014	J	`jnl`	A least-squares approach to anomaly detection in static and sequential data. Pattern Recognit. Lett. John A. Quinn, Masashi Sugiyama
2014	—	`conf`	An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions. ECML/PKDD (2) Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama
2014	A	`conf`	Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS Shinichi Nakajima, Masashi Sugiyama
2014	—	`conf`	Analysis of Learning from Positive and Unlabeled Data. NIPS Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2014	—	`conf`	Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. NIPS Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
2014	A	`conf`	Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. AISTATS Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee
2014	J	`jnl`	Class Prior Estimation from Positive and Unlabeled Data. IEICE Trans. Inf. Syst. Marthinus Christoffel du Plessis, Masashi Sugiyama
2014	—	`conf`	Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density. ECML/PKDD (3) Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama
2014	J	`jnl`	Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. IEICE Trans. Inf. Syst. Tomoya Sakai, Masashi Sugiyama
2014	J	`jnl`	Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. CoRR Voot Tangkaratt, Ning Xie, Masashi Sugiyama
2014	J	`jnl`	Constrained Least-Squares Density-Difference Estimation. IEICE Trans. Inf. Syst. Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Takafumi Kanamori, Masashi Sugiyama
2014	J	`jnl`	Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. Neural Comput. Song Liu, John A. Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi Sugiyama
2014	J	`jnl`	Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. CoRR Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
2014	—	`conf`	Efficient reuse of previous experiences in humanoid motor learning. Humanoids Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
2014	J	`jnl`	High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Comput. Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama
2014	J	`jnl`	Information-Maximization Clustering Based on Squared-Loss Mutual Information. Neural Comput. Masashi Sugiyama, Gang Niu, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya
2014	J	`jnl`	Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization. Neural Comput. Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
2014	J	`jnl`	Least-squares independence regression for non-linear causal inference under non-Gaussian noise. Mach. Learn. Makoto Yamada, Masashi Sugiyama, Jun Sese
2014	J	`jnl`	Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation. Neural Networks Voot Tangkaratt, Syogo Mori, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
2014	—	`conf`	Multitask learning meets tensor factorization: task imputation via convex optimization. NIPS Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
2014	A*	`conf`	Outlier Path: A Homotopy Algorithm for Robust SVM. ICML Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi
2014	J	`jnl`	Semi-supervised information-maximization clustering. Neural Networks Daniele Calandriello, Gang Niu, Masashi Sugiyama
2014	J	`jnl`	Semi-supervised learning of class balance under class-prior change by distribution matching. Neural Networks Marthinus Christoffel du Plessis, Masashi Sugiyama
2014	J	`jnl`	Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. Entropy Takafumi Kanamori, Masashi Sugiyama
2014	J	`jnl`	Support vector comparison machines. CoRR Toby Dylan Hocking, Supaporn Spanurattana, Masashi Sugiyama
2014	A*	`conf`	Transductive Learning with Multi-class Volume Approximation. ICML Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama
2014	J	`jnl`	Transductive Learning with Multi-class Volume Approximation. CoRR Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama
2014	J	`jnl`	Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression. IEICE Trans. Inf. Syst. Jaak Simm, Ildefons Magrans de Abril, Masashi Sugiyama
2014	J	`jnl`	Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information. IEICE Trans. Inf. Syst. Janya Sainui, Masashi Sugiyama
2013	J	`jnl`	Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. IEICE Trans. Inf. Syst. Ning Xie, Hirotaka Hachiya, Masashi Sugiyama
2013	J	`jnl`	Change-point detection in time-series data by relative density-ratio estimation. Neural Networks Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama
2013	J	`jnl`	Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances CoRR Marthinus Christoffel du Plessis, Masashi Sugiyama
2013	—	`conf`	Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances. TAAI Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2013	J	`jnl`	Computational complexity of kernel-based density-ratio estimation: a condition number analysis. Mach. Learn. Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2013	J	`jnl`	Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers. IEICE Trans. Inf. Syst. Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama
2013	J	`jnl`	Density Ratio Hidden Markov Models CoRR John A. Quinn, Masashi Sugiyama
2013	J	`jnl`	Density-Difference Estimation. Neural Comput. Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
2013	J	`jnl`	Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression. Inf. Media Technol. Akisato Kimura, Masashi Sugiyama, Hitoshi Sakano, Hirokazu Kameoka
2013	J	`jnl`	Detection of Activities and Events without Explicit Categorization. Inf. Media Technol. Masao Yamanaka, Masakazu Matsugu, Masashi Sugiyama
2013	—	`conf`	Direct Approximation of Divergences Between Probability Distributions. Empirical Inference Masashi Sugiyama
2013	J	`jnl`	Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering. IEICE Trans. Inf. Syst. Janya Sainui, Masashi Sugiyama
2013	J	`jnl`	Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. J. Comput. Sci. Eng. Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Masao Yamanaka, Makoto Yamada, Taiji Suzuki, Takafumi Kanamori
2013	—	`conf`	Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. ECML/PKDD (2) Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama
2013	—	`conf`	Divergence estimation for machine learning and signal processing. BCI Masashi Sugiyama
2013	J	`jnl`	Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. Neural Comput. Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
2013	J	`jnl`	Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration CoRR Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
2013	J	`jnl`	Feature Selection via l IEICE Trans. Inf. Syst. Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama
2013	—	`conf`	Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
2013	J	`jnl`	Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka
2013	—	`conf`	Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. ICML (3) Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama
2013	J	`jnl`	Machine Learning with Squared-Loss Mutual Information. Entropy Masashi Sugiyama
2013	J	`jnl`	Maximum volume clustering: a new discriminative clustering approach. J. Mach. Learn. Res. Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama
2013	J	`jnl`	Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation. CoRR Syogo Mori, Voot Tangkaratt, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
2013	J	`jnl`	Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis. Digit. Signal Process. Makoto Yamada, Gordon Wichern, Kazunobu Kondo, Masashi Sugiyama, Hiroshi Sawada
2013	—	`conf`	Parametric Task Learning. NIPS Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
2013	J	`jnl`	Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput. Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
2013	J	`jnl`	Salient Object Detection Based on Direct Density-ratio Estimation. Inf. Media Technol. Masao Yamanaka, Masakazu Matsugu, Masashi Sugiyama
2013	J	`jnl`	Semi-Supervised Information-Maximization Clustering CoRR Daniele Calandriello, Gang Niu, Masashi Sugiyama
2013	J	`jnl`	SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. Inf. Media Technol. Akisato Kimura, Masashi Sugiyama, Takuho Nakano, Hirokazu Kameoka, Hitoshi Sakano, Eisaku Maeda, Katsuhiko Ishiguro
2013	—	`conf`	Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. ICML (3) Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama
2013	J	`jnl`	Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation. Neural Comput. Taiji Suzuki, Masashi Sugiyama
2013	J	`jnl`	Variational Bayesian sparse additive matrix factorization. Mach. Learn. Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2013	J	`jnl`	Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering. IEICE Trans. Inf. Syst. Ildefons Magrans de Abril, Masashi Sugiyama
2012	J	`jnl`	Analysis and improvement of policy gradient estimation. Neural Networks Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
2012	J	`jnl`	Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting CoRR Ning Xie, Hirotaka Hachiya, Masashi Sugiyama
2012	A*	`conf`	Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. ICML Ning Xie, Hirotaka Hachiya, Masashi Sugiyama
2012	J	`jnl`	Canonical dependency analysis based on squared-loss mutual information. Neural Networks Masayuki Karasuyama, Masashi Sugiyama
2012	J	`jnl`	Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation CoRR Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama
2012	—	`conf`	Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation. SSPR/SPR Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama
2012	Misc	`conf`	Computationally efficient multi-label classification by least-squares probabilistic classifier. ICASSP Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama
2012	—	`book`	Density Ratio Estimation in Machine Learning. Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori
2012	J	`jnl`	Density-Difference Estimation CoRR Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
2012	—	`conf`	Density-Difference Estimation. NIPS Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
2012	J	`jnl`	Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information CoRR Makoto Yamada, Leonid Sigal, Michalis Raptis, Masashi Sugiyama
2012	J	`jnl`	Designing various component analysis at will CoRR Akisato Kimura, Masashi Sugiyama, Hitoshi Sakano, Hirokazu Kameoka
2012	B	`conf`	Designing various component analysis at will. ICPR Akisato Kimura, Hitoshi Sakano, Hirokazu Kameoka, Masashi Sugiyama
2012	J	`jnl`	Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier. IEICE Trans. Inf. Syst. Tsubasa Kobayashi, Masashi Sugiyama
2012	A	`conf`	Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness. AISTATS Taiji Suzuki, Masashi Sugiyama
2012	J	`jnl`	Feature Selection via L1-Penalized Squared-Loss Mutual Information CoRR Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama
2012	J	`jnl`	High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso CoRR Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Masashi Sugiyama
2012	J	`jnl`	Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing Hirotaka Hachiya, Masashi Sugiyama, Naonori Ueda
2012	J	`jnl`	Improving importance estimation in pool-based batch active learning for approximate linear regression. Neural Networks Nozomi Kurihara, Masashi Sugiyama
2012	J	`jnl`	Information-theoretic Semi-supervised Metric Learning via Entropy Regularization CoRR Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
2012	A*	`conf`	Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. ICML Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama
2012	J	`jnl`	Introduction to the Special Section on the 2nd Asia Conference on Machine Learning (ACML 2010). ACM Trans. Intell. Syst. Technol. Masashi Sugiyama, Qiang Yang
2012	—	`book`	Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation. Masashi Sugiyama, Motoaki Kawanabe
2012	J	`jnl`	Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems. IEICE Trans. Inf. Syst. Jaak Simm, Masashi Sugiyama, Hirotaka Hachiya
2012	J	`jnl`	Multi-parametric solution-path algorithm for instance-weighted support vector machines. Mach. Learn. Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi
2012	J	`jnl`	On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion. IEICE Trans. Inf. Syst. Masashi Sugiyama, Makoto Yamada
2012	J	`jnl`	Parametric Return Density Estimation for Reinforcement Learning CoRR Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka
2012	—	`conf`	Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
2012	J	`jnl`	Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching CoRR Marthinus Christoffel du Plessis, Masashi Sugiyama
2012	A*	`conf`	Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching. ICML Marthinus Christoffel du Plessis, Masashi Sugiyama
2012	J	`jnl`	Sequential change-point detection based on direct density-ratio estimation. Stat. Anal. Data Min. Yoshinobu Kawahara, Masashi Sugiyama
2012	C	`conf`	Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. ACML Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2012	J	`jnl`	Statistical analysis of kernel-based least-squares density-ratio estimation. Mach. Learn. Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2012	J	`jnl`	f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models. IEEE Trans. Inf. Theory Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2011	J	`jnl`	A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin. J. Mach. Learn. Res. Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng
2011	—	`ch.`	Active Learning in Recommender Systems. Recommender Systems Handbook Neil Rubens, Dain Kaplan, Masashi Sugiyama
2011	—	`conf`	Analysis and Improvement of Policy Gradient Estimation. NIPS Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
2011	Misc	`conf`	Automatic audio tag classification via semi-supervised canonical density estimation. ICASSP Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masashi Sugiyama, Makoto Yamada, Hirokazu Kameoka
2011	J	`jnl`	Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers. Inf. Media Technol. Jaak Simm, Masashi Sugiyama, Tsuyoshi Kato
2011	J	`jnl`	Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers. IPSJ Trans. Comput. Vis. Appl. Jaak Simm, Masashi Sugiyama, Tsuyoshi Kato
2011	A	`conf`	Cross-Domain Object Matching with Model Selection. AISTATS Makoto Yamada, Masashi Sugiyama
2011	J	`jnl`	Dependence-Maximization Clustering with Least-Squares Mutual Information. J. Adv. Comput. Intell. Intell. Informatics Manabu Kimura, Masashi Sugiyama
2011	—	`conf`	Detection of activities and events without explicit categorization. ICCV Workshops Masakazu Matsugu, Masao Yamanaka, Masashi Sugiyama
2011	A*	`conf`	Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis. AAAI Makoto Yamada, Masashi Sugiyama
2011	J	`jnl`	Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. Neural Networks Masashi Sugiyama, Makoto Yamada, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe
2011	J	`jnl`	Foreword. IEICE Trans. Inf. Syst. Masashi Sugiyama
2011	—	`conf`	Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. NIPS Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2011	J	`jnl`	Improving the Accuracy of Least-Squares Probabilistic Classifiers. IEICE Trans. Inf. Syst. Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Jaak Simm
2011	J	`jnl`	Least-Squares Independence Test. IEICE Trans. Inf. Syst. Masashi Sugiyama, Taiji Suzuki
2011	J	`jnl`	Least-Squares Independent Component Analysis. Neural Comput. Taiji Suzuki, Masashi Sugiyama
2011	J	`jnl`	Least-squares two-sample test. Neural Networks Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, Manabu Kimura
2011	J	`jnl`	Lighting Condition Adaptation for Perceived Age Estimation. IEICE Trans. Inf. Syst. Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara
2011	A	`conf`	Maximum Volume Clustering. AISTATS Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama
2011	—	`conf`	Multi-parametric solution-path algorithm for instance-weighted support vector machines. MLSP Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi
2011	—	`conf`	Multi-race age estimation based on the combination of multiple classifiers. ACPR Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara, Mitsuhiro Fujita
2011	A*	`conf`	On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. ICML Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2011	A*	`conf`	On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution. ICML Masashi Sugiyama, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya
2011	—	`conf`	Relative Density-Ratio Estimation for Robust Distribution Comparison. NIPS Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
2011	J	`jnl`	Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning. Neural Comput. Hirotaka Hachiya, Jan Peters, Masashi Sugiyama
2011	J	`jnl`	Statistical outlier detection using direct density ratio estimation. Knowl. Inf. Syst. Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
2011	C	`conf`	Suffcient Component Analysis. ACML Makoto Yamada, Gang Niu, Jun Takagi, Masashi Sugiyama
2011	J	`jnl`	Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. J. Mach. Learn. Res. Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama
2011	—	`conf`	Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification. NIPS Ichiro Takeuchi, Masashi Sugiyama
2011	J	`jnl`	Theoretical Analysis of Bayesian Matrix Factorization. J. Mach. Learn. Res. Shinichi Nakajima, Masashi Sugiyama
2011	A*	`conf`	Trajectory Regression on Road Networks. AAAI Tsuyoshi Idé, Masashi Sugiyama
2010	A*	`conf`	A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. ICML Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima
2010	J	`jnl`	A Semi-Supervised Approach to Perceived Age Prediction from Face Images. IEICE Trans. Inf. Syst. Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara
2010	J	`jnl`	A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference. Int. J. Knowl. Discov. Bioinform. Tsuyoshi Kato, Kinya Okada, Hisashi Kashima, Masashi Sugiyama
2010	Misc	`conf`	Acceleration of sequence kernel computation for real-time speaker identification. ICASSP Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Tomoko Matsui
2010	J	`jnl`	Application of Covariate Shift Adaptation Techniques in Brain-Computer Interfaces. IEEE Trans. Biomed. Eng. Yan Li, Hiroyuki Kambara, Yasuharu Koike, Masashi Sugiyama
2010	Misc	`conf`	Automatic audio tagging using covariate shift adaptation. ICASSP Gordon Wichern, Makoto Yamada, Harvey D. Thornburg, Masashi Sugiyama, Andreas Spanias
2010	A	`conf`	Conditional Density Estimation via Least-Squares Density Ratio Estimation. AISTATS Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
2010	J	`jnl`	Conic Programming for Multitask Learning. IEEE Trans. Knowl. Data Eng. Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
2010	A*	`conf`	Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise. AAAI Makoto Yamada, Masashi Sugiyama
2010	J	`jnl`	Dimensionality reduction for density ratio estimation in high-dimensional spaces. Neural Networks Masashi Sugiyama, Motoaki Kawanabe, Pui Ling Chui
2010	A	`conf`	Direct Density Ratio Estimation with Dimensionality Reduction. SDM Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe
2010	J	`jnl`	Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers. IEICE Trans. Inf. Syst. Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Jaak Simm
2010	Misc	`conf`	Direct importance estimation with probabilistic principal component analyzers. ICASSP Makoto Yamada, Masashi Sugiyama, Gordon Wichern
2010	J	`jnl`	Efficient exploration through active learning for value function approximation in reinforcement learning. Neural Networks Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama
2010	—	`conf`	Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information. ECML/PKDD (1) Hirotaka Hachiya, Masashi Sugiyama
2010	J	`jnl`	Foreword. IEICE Trans. Inf. Syst. Masashi Sugiyama
2010	—	`conf`	Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka
2010	A*	`conf`	Implicit Regularization in Variational Bayesian Matrix Factorization. ICML Shinichi Nakajima, Masashi Sugiyama
2010	J	`jnl`	Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation. IEICE Trans. Inf. Syst. Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura
2010	J	`jnl`	Least-Squares Conditional Density Estimation. IEICE Trans. Inf. Syst. Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
2010	J	`jnl`	Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines CoRR Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi
2010	A*	`conf`	Nonparametric Return Distribution Approximation for Reinforcement Learning. ICML Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka
2010	J	`jnl`	Output Divergence Criterion for Active Learning in Collaborative Settings. Inf. Media Technol. Neil Rubens, Ryota Tomioka, Masashi Sugiyama
2010	A	`conf`	Parametric Return Density Estimation for Reinforcement Learning. UAI Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka
2010	B	`conf`	Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation. ICPR Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara
2010	C	`conf`	Preface. ACML Masashi Sugiyama, Qiang Yang
2010	C	`ed.`	Proceedings of the 2nd Asian Conference on Machine Learning, ACML 2010, Tokyo, Japan, November 8-10, 2010 ACML Masashi Sugiyama, Qiang Yang
2010	—	`conf`	Semi-supervised Estimation of Perceived Age from Face Images. VISAPP (2) Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara
2010	J	`jnl`	Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach. Learn. Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
2010	J	`jnl`	Semi-supervised speaker identification under covariate shift. Signal Process. Makoto Yamada, Masashi Sugiyama, Tomoko Matsui
2010	B	`conf`	SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. ICPR Akisato Kimura, Hirokazu Kameoka, Masashi Sugiyama, Takuho Nakano, Eisaku Maeda, Hitoshi Sakano, Katsuhiko Ishiguro
2010	C	`conf`	Single versus Multiple Sorting in All Pairs Similarity Search. ACML Yasuo Tabei, Takeaki Uno, Masashi Sugiyama, Koji Tsuda
2010	J	`jnl`	Spectral Methods for Thesaurus Construction. IEICE Trans. Inf. Syst. Nobuyuki Shimizu, Masashi Sugiyama, Hiroshi Nakagawa
2010	A	`conf`	Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation. AISTATS Taiji Suzuki, Masashi Sugiyama
2010	J	`jnl`	Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting. IEICE Trans. Inf. Syst. Masashi Sugiyama
2010	J	`jnl`	Theoretical Analysis of Density Ratio Estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2009	J	`jnl`	A Density-ratio Framework for Statistical Data Processing. Inf. Media Technol. Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang
2009	J	`jnl`	A Density-ratio Framework for Statistical Data Processing. IPSJ Trans. Comput. Vis. Appl. Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang
2009	—	`conf`	A Framework of Adaptive Brain Computer Interfaces. BMEI Yan Li, Yasuharu Koike, Masashi Sugiyama
2009	J	`jnl`	A Least-squares Approach to Direct Importance Estimation. J. Mach. Learn. Res. Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
2009	A*	`conf`	Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. IJCAI Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama
2009	J	`jnl`	Adaptive importance sampling for value function approximation in off-policy reinforcement learning. Neural Networks Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters
2009	B	`conf`	Analysis of Variational Bayesian Matrix Factorization. PAKDD Shinichi Nakajima, Masashi Sugiyama
2009	A	`conf`	Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. SDM Yoshinobu Kawahara, Masashi Sugiyama
2009	Misc	`conf`	Covariate shift adaptation for semi-supervised speaker identification. ICASSP Makoto Yamada, Masashi Sugiyama, Tomoko Matsui
2009	C	`conf`	Density Ratio Estimation: A New Versatile Tool for Machine Learning. ACML Masashi Sugiyama
2009	J	`jnl`	Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. Inf. Media Technol. Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama
2009	J	`jnl`	Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. J. Inf. Process. Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama
2009	J	`jnl`	Direct Importance Estimation with Gaussian Mixture Models. IEICE Trans. Inf. Syst. Makoto Yamada, Masashi Sugiyama
2009	J	`jnl`	Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction. IEEE Signal Process. Lett. Ryota Tomioka, Masashi Sugiyama
2009	J	`jnl`	Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method CoRR Takeaki Uno, Masashi Sugiyama, Koji Tsuda
2009	—	`conf`	Efficient Sample Reuse in EM-Based Policy Search. ECML/PKDD (1) Hirotaka Hachiya, Jan Peters, Masashi Sugiyama
2009	—	`conf`	Efficient data reuse in value function approximation. ADPRL Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters
2009	—	`conf`	Estimating Squared-Loss Mutual Information for Independent Component Analysis. ICA Taiji Suzuki, Masashi Sugiyama
2009	A	`conf`	Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression. AISTATS Nicole Krämer, Masashi Sugiyama, Mikio L. Braun
2009	A*	`conf`	Least absolute policy iteration for robust value function approximation. ICRA Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura
2009	A	`conf`	Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. SDM Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, Koji Tsuda
2009	B	`conf`	Mutual information approximation via maximum likelihood estimation of density ratio. ISIT Taiji Suzuki, Masashi Sugiyama, Toshiyuki Tanaka
2009	J	`jnl`	Mutual information estimation reveals global associations between stimuli and biological processes. BMC Bioinform. Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese
2009	J	`jnl`	On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis. IEICE Trans. Inf. Syst. Masashi Sugiyama
2009	J	`jnl`	On Generalization Performance and Non-Convex Optimization of Extended New Gener. Comput. Akiko Takeda, Masashi Sugiyama
2009	J	`jnl`	Pool-based active learning in approximate linear regression. Mach. Learn. Masashi Sugiyama, Shinichi Nakajima
2009	B	`conf`	Probabilistic principal component analysis based on JoyStick Probability Selector. IJCNN Marko V. Jankovic, Masashi Sugiyama
2009	J	`jnl`	Recent Advances and Trends in Large-Scale Kernel Methods. IEICE Trans. Inf. Syst. Hisashi Kashima, Tsuyoshi Idé, Tsuyoshi Kato, Masashi Sugiyama
2009	J	`jnl`	Robust Label Propagation on Multiple Networks. IEEE Trans. Neural Networks Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama
2009	J	`jnl`	Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. Bioinform. Hisashi Kashima, Yoshihiro Yamanishi, Tsuyoshi Kato, Masashi Sugiyama, Koji Tsuda
2009	J	`jnl`	Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation. CoRR Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama
2009	J	`jnl`	Theory and Algorithm for Learning with Dissimilarity Functions. Neural Comput. Liwei Wang, Masashi Sugiyama, Cheng Yang, Kohei Hatano, Jufu Feng
2008	J	`jnl`	A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule. IEEE Signal Process. Lett. Marko V. Jankovic, Masashi Sugiyama
2008	J	`jnl`	A batch ensemble approach to active learning with model selection. Neural Networks Masashi Sugiyama, Neil Rubens
2008	A	`conf`	Active Learning with Model Selection in Linear Regression. SDM Masashi Sugiyama, Neil Rubens
2008	A*	`conf`	Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. AAAI Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters
2008	—	`conf`	Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. FSDM Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori
2008	J	`jnl`	Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise. IEICE Trans. Inf. Syst. Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Klaus-Robert Müller
2008	A	`conf`	Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. SDM Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama
2008	—	`conf`	Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. NIPS Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
2008	J	`jnl`	Geodesic Gaussian kernels for value function approximation. Auton. Robots Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar
2008	A*	`conf`	Inlier-Based Outlier Detection via Direct Density Ratio Estimation. ICDM Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
2008	A	`conf`	Integration of Multiple Networks for Robust Label Propagation. SDM Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama
2008	A*	`conf`	On the Margin Explanation of Boosting Algorithms. COLT Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng
2008	—	`conf`	Order Retrieval. LKR Neil Rubens, Vera Sheinman, Takenobu Tokunaga, Masashi Sugiyama
2008	—	`conf`	Pool-Based Agnostic Experiment Design in Linear Regression. ECML/PKDD (2) Masashi Sugiyama, Shinichi Nakajima
2008	B	`conf`	Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. PAKDD Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
2007	J	`jnl`	A New Meta-Criterion for Regularized Subspace Information Criterion. IEICE Trans. Inf. Syst. Yasushi Hidaka, Masashi Sugiyama
2007	J	`jnl`	Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Shun Gokita, Masashi Sugiyama, Keisuke Sakurai
2007	A*	`conf`	Asymptotic Bayesian generalization error when training and test distributions are different. ICML Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller
2007	J	`jnl`	Covariate Shift Adaptation by Importance Weighted Cross Validation. J. Mach. Learn. Res. Masashi Sugiyama, Matthias Krauledat, Klaus-Robert Müller
2007	J	`jnl`	Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. J. Mach. Learn. Res. Masashi Sugiyama
2007	—	`conf`	Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. NIPS Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe
2007	J	`jnl`	Generalization Error Estimation for Non-linear Learning Methods. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Masashi Sugiyama
2007	A	`conf`	Influence-based collaborative active learning. RecSys Neil Rubens, Masashi Sugiyama
2007	—	`conf`	Multi-Task Learning via Conic Programming. NIPS Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
2007	A*	`conf`	Value Function Approximation on Non-Linear Manifolds for Robot Motor Control. ICRA Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar
2006	—	`conf`	A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces. ICA Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller
2006	J	`jnl`	Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. J. Mach. Learn. Res. Masashi Sugiyama
2006	J	`jnl`	Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Masashi Sugiyama, Keisuke Sakurai
2006	J	`jnl`	Constructing Kernel Functions for Binary Regression. IEICE Trans. Inf. Syst. Masashi Sugiyama, Hidemitsu Ogawa
2006	—	`conf`	Importance-Weighted Cross-Validation for Covariate Shift. DAGM-Symposium Masashi Sugiyama, Benjamin Blankertz, Matthias Krauledat, Guido Dornhege, Klaus-Robert Müller
2006	J	`jnl`	In Search of Non-Gaussian Components of a High-Dimensional Distribution. J. Mach. Learn. Res. Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller
2006	A*	`conf`	Local Fisher discriminant analysis for supervised dimensionality reduction. ICML Masashi Sugiyama
2006	—	`conf`	Mixture Regression for Covariate Shift. NIPS Amos J. Storkey, Masashi Sugiyama
2006	—	`conf`	Model Selection Using a Class of Kernels with an Invariant Metric. SSPR/SPR Akira Tanaka, Masashi Sugiyama, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi
2006	—	`conf`	Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis. ICASSP (3) Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Vladimir G. Spokoiny, Klaus-Robert Müller
2005	—	`conf`	Active Learning for Misspecified Models. NIPS Masashi Sugiyama
2005	—	`conf`	Model Selection Under Covariate Shift. ICANN (2) Masashi Sugiyama, Klaus-Robert Müller
2005	—	`conf`	Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. NIPS Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir G. Spokoiny, Klaus-Robert Müller
2004	—	`conf`	Estimating the error at given test input points for linear regression. Neural Networks and Computational Intelligence Masashi Sugiyama
2004	B	`conf`	Regularizing generalization error estimators: a novel approach to robust model selection. ESANN Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller
2004	J	`jnl`	Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression. Neural Comput. Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller
2003	J	`jnl`	Improving Precision of the Subspace Information Criterion. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Masashi Sugiyama
2002	J	`jnl`	A unified method for optimizing linear image restoration filters. Signal Process. Masashi Sugiyama, Hidemitsu Ogawa
2002	J	`jnl`	Optimal design of regularization term and regularization parameter by subspace information criterion. Neural Networks Masashi Sugiyama, Hidemitsu Ogawa
2002	C	`conf`	Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces. ICANN Masashi Sugiyama, Klaus-Robert Müller
2002	J	`jnl`	Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors. IEEE Trans. Neural Networks Koji Tsuda, Masashi Sugiyama, Klaus-Robert Müller
2002	J	`jnl`	The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces. J. Mach. Learn. Res. Masashi Sugiyama, Klaus-Robert Müller
2002	J	`jnl`	Theoretical and Experimental Evaluation of the Subspace Information Criterion. Mach. Learn. Masashi Sugiyama, Hidemitsu Ogawa
2001	J	`jnl`	Incremental Active Learning for Optimal Generalization. Neural Comput. Masashi Sugiyama, Hidemitsu Ogawa
2001	J	`jnl`	Incremental projection learning for optimal generalization. Neural Networks Masashi Sugiyama, Hidemitsu Ogawa
2001	J	`jnl`	Properties of incremental projection learning. Neural Networks Masashi Sugiyama, Hidemitsu Ogawa
2001	J	`jnl`	Subspace Information Criterion for Model Selection. Neural Comput. Masashi Sugiyama, Hidemitsu Ogawa
2000	B	`conf`	A new information criterion for the selection of subspace models. ESANN Masashi Sugiyama, Hidemitsu Ogawa
2000	—	`conf`	Incremental Active Learning with Bias Reduction. IJCNN (1) Masashi Sugiyama, Hidemitsu Ogawa
1999	—	`conf`	Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. NIPS Masashi Sugiyama, Hidemitsu Ogawa