Masashi Sugiyama - My portal

Profile Information

Affiliation: Director, Center for Advanced Intelligence Project, RIKEN
: Professor, Graduate School of Frontier Sciences, The University of Tokyo

Degree: Doctor of Engineering(Mar, 2001, Tokyo Institute of Technology)

Researcher number: 90334515
ORCID ID: https://orcid.org/0000-0001-6658-6743
J-GLOBAL ID: 201201034649653220
researchmap Member ID: B000222682

External link: http://www.ms.k.u-tokyo.ac.jp/sugi/

http://www.ms.k.u-tokyo.ac.jp/sugi/profile.html

Research Areas

Informatics / Intelligent informatics /

Research History

Jul, 2016 - Present

Director, Center for Advanced Intelligence Project, RIKEN
Oct, 2014 - Present

Professor, Graduate School of Frontier Sciences, The University of Tokyo

Papers

555

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning.

Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama

CoRR, abs/2305.18377, 2023
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks.

Jingfeng Zhang, Bo Song, Bo Han 0003, Lei Liu, Gang Niu 0001, Masashi Sugiyama

CoRR, abs/2305.00399, 2023
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization.

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli

CoRR, abs/2305.00374, 2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection.

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli

CoRR, abs/2302.03857, 2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks.

Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon

CoRR, abs/2302.02907, 2023
Discovering diverse solutions in deep reinforcement learning by maximizing state–action-based mutual information

Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

Neural Networks, 152 90-104, Aug, 2022
Deep learning, reinforcement learning, and world models

Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto

Neural Networks, 152 267-275, Aug, 2022
Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding.

Mugihiko Kato, Toshiki Okumura, Yasuhiro Tsubo, Junya Honda, Masashi Sugiyama, Kazushige Touhara, Masako Okamoto

Proceedings of the National Academy of Sciences of the United States of America, 119(21) e2114966119, May 24, 2022
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness.

Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han 0003, Masashi Sugiyama, Changshui Zhang

NeurIPS, 2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks.

Jianan Zhou 0002, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu 0001, Bo Han 0003, Masashi Sugiyama

NeurIPS, 2022
NoiLin: Improving adversarial training and correcting stereotype of noisy labels.

Jingfeng Zhang, Xilie Xu, Bo Han 0003, Tongliang Liu, Lizhen Cui, Gang Niu 0001, Masashi Sugiyama

Trans. Mach. Learn. Res., 2022, 2022
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning.

Tingting Zhao 0001, Ying Wang, Wei Sun, Yarui Chen, Gang Niub, Masashi Sugiyama

CoRR, abs/2211.13257, 2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks.

Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu 0001, Bo Han 0003, Masashi Sugiyama

CoRR, abs/2211.00269, 2022
Audio Signal Enhancement with Learning from Positive and Unlabelled Data.

Nobutaka Ito, Masashi Sugiyama

CoRR, abs/2210.15143, 2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation.

De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang 0001, Bo Han 0003, Gang Niu 0001, Xinbo Gao 0001, Masashi Sugiyama

CVPR, 16609-16618, 2022
Learning and Mining with Noisy Labels.

Masashi Sugiyama, Tongliang Liu, Bo Han 0003, Yang Liu, Gang Niu 0001

CIKM, 5152-5155, 2022
Neural Networks special issue on Artificial Intelligence and Brain Science.

Kenji Doya, Karl J. Friston, Masashi Sugiyama, Joshua B. Tenenbaum

Neural Networks, 155 328-329, 2022
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift.

Yivan Zhang, Jindong Wang, Xing Xie 0001, Masashi Sugiyama

CoRR, abs/2208.02011, 2022
Adapting to Online Label Shift with Provable Guarantees.

Yong Bai, Yu-Jie Zhang, Peng Zhao 0006, Masashi Sugiyama, Zhi-Hua Zhou

CoRR, abs/2207.02121, 2022
Learning from Multiple Unlabeled Datasets with Partial Risk Regularization.

Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama

CoRR, abs/2207.01555, 2022

Misc.

255

Constraint learning for control tasks with limited duration barrier functions

Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt

Automatica, 127 109504-109504, May, 2021
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.

Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama

Neural computation, 33(5) 1-35, Feb 22, 2021
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)

江間有沙, 木谷強, 石黒浩, 杉山将, 西野恒, 田丸健三郎, Papaspyridis Alexandros

大学マネジメント = University & college management, 15(12) 29-33, Mar, 2020
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)

杉山将

大学マネジメント = University & college management, 15(12) 19-24, Mar, 2020
Failure factor detection in production process

OTSUBO Yosuke, OTANI Naoya, CHIKASUE Megumi, SUGIYAMA Masashi

Proceedings of the Annual Conference of JSAI, 2020 2I4GS204-2I4GS204, 2020
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia.

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

CoRR, abs/2006.15815, 2020
Are Registration Uncertainty and Error Monotonically Associated?

Jie Luo 0003, Sarah F. Frisken, Duo Wang, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12263 LNCS 264-274, 2020
Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback.

Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi 0001, Junya Honda, Masashi Sugiyama

Neural Comput., 32(9) 1733-1773, 2020
A One-step Approach to Covariate Shift Adaptation.

Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama

Proceedings of The 12th Asian Conference on Machine Learning(ACML), 65-80, 2020
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels.

Yu-Ting Chou, Gang Niu 0001, Hsuan-Tien Lin, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning(ICML), 1929-1938, 2020
Online Dense Subgraph Discovery via Blurred-Graph Feedback.

Yuko Kuroki, Atsushi Miyauchi 0001, Junya Honda, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning(ICML), 5522-5532, 2020
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent.

Mehdi Abbana Bennani, Masashi Sugiyama

CoRR, abs/2006.11942, 2020
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators.

Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama

Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020(NeurIPS), 2020
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring.

Taira Tsuchiya, Junya Honda, Masashi Sugiyama

Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020(NeurIPS), 2020
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning.

Kei Mukaiyama, Issei Sato, Masashi Sugiyama

CoRR, abs/2006.08306, 2020
Part-dependent Label Noise: Towards Instance-dependent Label Noise.

Xiaobo Xia, Tongliang Liu, Bo Han 0003, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu 0001, Dacheng Tao, Masashi Sugiyama

Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020(NeurIPS), 2020
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning.

Yu Yao, Tongliang Liu, Bo Han 0003, Mingming Gong, Jiankang Deng, Gang Niu 0001, Masashi Sugiyama

Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020(NeurIPS), 2020
Similarity-based Classification: Connecting Similarity Learning to Binary Classification.

Han Bao 0002, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama

CoRR, abs/2006.06207, 2020
Variational Imitation Learning with Diverse-quality Demonstrations.

Voot Tangkaratt, Bo Han 0003, Mohammad Emtiyaz Khan, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning(ICML), 9407-9417, 2020
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis.

Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning(ICML), 9636-9647, 2020

Books and Other Publications

ディープラーニングG検定ジェネラリスト問題集

スキルアップAI, 明松, 真司, 田原, 眞一, 杉山, 将, ソキウス・ジャパン

インプレス, 2019 (ISBN: 9784295005667)
Variational Bayesian learning theory

中島, 伸一, 渡辺, 一帆, 杉山, 将

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

須山, 敦志, 杉山, 将, 講談社サイエンティフィク

講談社, Oct, 2017 (ISBN: 9784061538320)
異常検知と変化検知 = Anomaly detection and change detection

井手, 剛, 杉山, 将

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

杉山, 将

講談社, 2015 (ISBN: 9784061529014)
統計的学習の基礎 : データマイニング・推論・予測

Hastie, T. J, Tibshirani, R. J., Friedman, J. H. (Jerome H.), 井尻, 善久, 杉山, 将, 井手, 剛, 神嶌, 敏弘, 栗田, 多喜夫, 前田, 英作

共立出版, 2014 (ISBN: 9784320123625)
イラストで学ぶ機械学習 : 最小二乗法による識別モデル学習を中心に

杉山, 将, 講談社サイエンティフィク

講談社, 2013 (ISBN: 9784061538214)
Density ratio estimation in machine learning

杉山, 将, 鈴木, 大慈, 金森, 敬文

Cambridge University Press, 2012 (ISBN: 9780521190176)
統計的機械学習 : 生成モデルに基づくパターン認識

杉山, 将

オーム社, 2009 (ISBN: 9784274502484)
強くなるロボティック・ゲームプレイヤーの作り方 : 実践で学ぶ強化学習

八谷, 大岳, 杉山, 将

毎日コミュニケーションズ, 2008 (ISBN: 9784839927417)
基礎から学べる統計数理とその実応用 : 大規模データ処理による推論・予測:機械学習,ベイジアンネットワーク,粒子フィルタ

石井, 信(情報学), 杉山, 将, 本村, 陽一, 樋口, 知之, システム制御情報学会

システム制御情報学会 (ISBN: 9784915740510)
Machine learning in non-stationary environments : introduction to covariate shift adaptation

杉山, 将, Kawanabe, Motoaki

MIT Press (ISBN: 9780262017091)
Statistical reinforcement learning : modern machine learning approaches

杉山, 将

CRC press (ISBN: 9781439856895)
Introduction to statistical machine learning

杉山, 将

Morgan Kaufmann (ISBN: 9780128021217)

Research Projects

Theory for unified expression of recognition mechanisms and its application to machine learning

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, Apr, 2020 - Mar, 2023
Theory and Application of Statistical Reinforcement Learning

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A), Grant-in-Aid for Scientific Research (A), Japan Society for the Promotion of Science, Apr, 2017 - Mar, 2022

Sugiyama Masashi
Theory of operator manifold and its application to pattern recognition

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, Apr, 2017 - Mar, 2020

Yukihiko Yamashita
Manifold signal processing theory concerning metric structure and its application to biological signal processing

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, Apr, 2014 - Mar, 2017

Yukihiko Yamashita
Theory and Application of Information-Based Machine Learning

Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (A), Grant-in-Aid for Young Scientists (A), Japan Society for the Promotion of Science, Apr, 2013 - Mar, 2017

Sugiyama Masashi
Elucidation of the Neral Computation for Prediction and Decision Making

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area), Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area), Japan Society for the Promotion of Science, Apr, 2011 - Mar, 2016

Doya Kenji, SAKAGAMI Masamichi, OKAMOTO Hitoshi, SHIBATA Tomohiro, OKADA Mitsuhiro, HIKIDA Takatoshi, SUGIYAMA Masashi, KIMURA Minoru, IMAI Mutsumi, TAKAHASHI Hidehiko
Development of Machine Learning Theory for Prediction and Decision Making and Its Realization in Neural Networks

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area), Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area), Japan Society for the Promotion of Science, Apr, 2011 - Mar, 2016

Sugiyama Masashi, MORIMOTO Jun
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

科学研究費助成事業特別研究員奨励費, 特別研究員奨励費, 日本学術振興会, Apr, 2014 - Mar, 2015

杉山将, YGER Florian
signal manifold by extended kernel method and its application

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, Apr, 2011 - Mar, 2014

YUKIHIKO Yamashita, SUGIYAMA Masashi, TANAKA Toshihisa, WASHIZAWA Yoshikazu
Machine Learning under Changing Environments

Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (A), Grant-in-Aid for Young Scientists (A), Japan Society for the Promotion of Science, 2008 - 2011

SUGIYAMA Masashi
生物的神経回路網の理論とその応用

科学研究費助成事業特別研究員奨励費, 特別研究員奨励費, 日本学術振興会, 2007 - 2009

杉山将, JANKOVIC Marko
Machine learning theory based on structure of signal space and its application

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, 2006 - 2008

YAMASHITA Yukihiko, SUGIYAMA Masashi, TANAKA Toshihisa, WASHIZAWA Yoshikazu
入力点に依存した汎化能力推定法に関する研究

科学研究費助成事業若手研究(B), 若手研究(B), 日本学術振興会, 2005 - 2007

杉山将
Theory of Family of Learnings-From a Single Learning to Infinitely Many Learning-

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, 2002 - 2004

OGAWA Hidemitsu, KUMAZAWA Itsuo
教師付き学習におけるモデル選択に関する研究

科学研究費助成事業若手研究(B), 若手研究(B), 日本学術振興会, 2002 - 2004

杉山将
Generalization Capability of Memorization Leaning

Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Japan Society for the Promotion of Science, 1999 - 2001

OGAWA Hidemitsu, SUGIYAMA Masashi, HIRABAYASHI Akira, KUMAZAWA Itsuo