Rchr
J-GLOBAL ID:201201034649653220   Update date: Oct. 31, 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.htmlhttp://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
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Papers (560):
  • 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
  • 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. 2024
  • Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama. Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML. 2023. 18810-18851
  • Jongyeong Lee, Junya Honda, Masashi Sugiyama. Thompson Exploration with Best Challenger Rule in Best Arm Identification. ACML. 2023. 646-661
  • Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama. BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR. 2023. abs/2305.18377
more...
MISC (256):
  • Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt. Constraint learning for control tasks with limited duration barrier functions. Automatica. 2021. 127. 109504-109504
  • 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
  • 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
  • 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
  • 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):
  • 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
  • Variational Bayesian learning theory
    Cambridge University Press 2019 ISBN:9781107076150
  • 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
  • 異常検知と変化検知 = Anomaly detection and change detection
    講談社 2015 ISBN:9784061529083
  • 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
more...
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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