Rchr
J-GLOBAL ID：201201034649653220
Update date: Dec. 04, 2024
Sugiyama Masashi
スギヤマ マサシ | Sugiyama Masashi
Affiliation and department：
Job title：
Director
Other affiliations (1)：
Homepage URL (2)：
http://www.ms.k.u-tokyo.ac.jp/sugi/index-jp.html
,
http://www.ms.k.u-tokyo.ac.jp/sugi/
Research field (1)：
Intelligent informatics
Research theme for competitive and other funds (16)：
- 2020 - 2023 Theory for unified expression of recognition mechanisms and its application to machine learning
- 2017 - 2022 Theory and Application of Statistical Reinforcement Learning
- 2017 - 2020 Theory of operator manifold and its application to pattern recognition
- 2014 - 2017 Manifold signal processing theory concerning metric structure and its application to biological signal processing
- 2013 - 2017 Theory and Application of Information-Based Machine Learning
- 2011 - 2016 Elucidation of the Neral Computation for Prediction and Decision Making
- 2011 - 2016 Development of Machine Learning Theory for Prediction and Decision Making and Its Realization in Neural Networks
- 2014 - 2015 時間的な変化を伴うデータに対する機械学習手法に関する研究
- 2011 - 2014 signal manifold by extended kernel method and its application
- 2008 - 2011 Machine Learning under Changing Environments
- 2007 - 2009 生物的神経回路網の理論とその応用
- 2006 - 2008 Machine learning theory based on structure of signal space and its application
- 2005 - 2007 入力点に依存した汎化能力推定法に関する研究
- 2002 - 2004 Theory of Family of Learnings-From a Single Learning to Infinitely Many Learning-
- 2002 - 2004 教師付き学習におけるモデル選択に関する研究
- 1999 - 2001 Generalization Capability of Memorization Leaning
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Papers (561)：
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Jia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang. Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-supervised Multi-label Learning. 2025
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Wenshui Luo, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong. Estimating Per-Class Statistics for Label Noise Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2025
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Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen, Ning Xie, Gang Niu, Masashi Sugiyama. Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks. 2024
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Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama. Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML. 2023. 18810-18851
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Jongyeong Lee, Junya Honda, Masashi Sugiyama. Thompson Exploration with Best Challenger Rule in Best Arm Identification. ACML. 2023. 646-661
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MISC (256)：
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Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt. Constraint learning for control tasks with limited duration barrier functions. Automatica. 2021. 127. 109504-109504
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Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama. Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural computation. 2021. 33. 5. 1-35
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江間 有沙, 木谷 強, 石黒 浩, 杉山 将, 西野 恒, 田丸 健三郎, Papaspyridis Alexandros. パネルディスカッション AIの社会実装とAI人材の育成に向けて必要なこと : 大学とAIエコシステム (特集 AI活用ができる人材育成に必要な大学変革と役割) -- (AIアカデミックフォーラム2020). 大学マネジメント = University & college management. 2020. 15. 12. 29-33
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杉山 将. 世界をリードするAI研究と人材育成への挑戦 (特集 AI活用ができる人材育成に必要な大学変革と役割) -- (AIアカデミックフォーラム2020). 大学マネジメント = University & college management. 2020. 15. 12. 19-24
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OTSUBO Yosuke, OTANI Naoya, CHIKASUE Megumi, SUGIYAMA Masashi. Failure factor detection in production process. Proceedings of the Annual Conference of JSAI. 2020. 2020. 0. 2I4GS204-2I4GS204
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Books (14)：
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ディープラーニングG検定ジェネラリスト問題集
インプレス 2019 ISBN:9784295005667
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Variational Bayesian learning theory
Cambridge University Press 2019 ISBN:9781107076150
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ベイズ推論による機械学習入門 = Introduction to machine learning by Bayesian inference
講談社 2017 ISBN:9784061538320
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異常検知と変化検知 = Anomaly detection and change detection
講談社 2015 ISBN:9784061529083
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機械学習のための確率と統計 = Probability and statistics for machine learning
講談社 2015 ISBN:9784061529014
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Professional career (1)：
- Doctor of Engineering (Tokyo Institute of Technology)
Work history (2)：
- 2016/07 - 現在 RIKEN Center for Advanced Intelligence Project Director
- 2014/10 - 現在 The University of Tokyo Graduate School of Frontier Sciences Professor
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