World's Best Scientists 2026 revealed!
Masashi Sugiyama

Masashi Sugiyama

Award Badge
Computer Science
Japan
2026

D-Index & Metrics

Computer Science

D-Index
81
Citations
30891
World Ranking
1003
National Ranking
3

Research.com Recognitions

  • 2026 - Research.com Computer Science in Japan Leader Award
  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Masashi Sugiyama is affiliated with RIKEN in Japan and specializes in the field of computer science, with a significant focus on artificial intelligence. Their research spans several subfields including computer vision and pattern recognition, management science and operations research, computer networks and communications, and civil and structural engineering.

The scientist's work extensively covers themes related to machine learning and data classification, domain adaptation and few-shot learning, and machine learning algorithms. Other prominent topics include anomaly detection techniques and applications, adversarial robustness in machine learning, imbalanced data classification techniques, and advanced bandit algorithms research.

Masashi Sugiyama has a strong record of publications in a variety of venues:

  • arXiv (Cornell University)
  • Neural Computation
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Neural Networks
  • Zenodo (CERN European Organization for Nuclear Research)

Recent papers authored or co-authored by Sugiyama include:

  • "Deep learning, reinforcement learning, and world models", 2022, published in Neural Networks
  • "Part-dependent Label Noise: Towards Instance-dependent Label Noise", 2020, published in arXiv (Cornell University)
  • "Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review", 2024, published in Journal of Medical Systems
  • "Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning", 2020, published in arXiv (Cornell University)
  • "A Survey of Label-noise Representation Learning: Past, Present and Future", 2020, published in arXiv (Cornell University)

Sugiyama collaborates frequently with several researchers. The main frequent co-authors include:

  • Gang Niu
  • Tongliang Liu
  • Bo Han
  • Jingfeng Zhang

Best Publications

  • Dataset Shift in Machine Learning

    Joaquin Quionero-Candela;Masashi Sugiyama;Anton Schwaighofer;Neil D. Lawrence

  • Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

    Bo Han;Quanming Yao;Xingrui Yu;Gang Niu

  • Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

    Masashi Sugiyama

  • Covariate Shift Adaptation by Importance Weighted Cross Validation

    Masashi Sugiyama;Matthias Krauledat;Klaus-Robert Müller

  • Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

    Masashi Sugiyama;Shinichi Nakajima;Hisashi Kashima;Paul V. Buenau

  • Covariate Shift by Kernel Mean Matching

    A Gretton;AJ Smola;J Huang;M Schmittfull

  • Direct importance estimation for covariate shift adaptation

    Masashi Sugiyama;Taiji Suzuki;Shinichi Nakajima;Hisashi Kashima

  • Change-point detection in time-series data by relative density-ratio estimation

    Song Liu;Makoto Yamada;Nigel Collier;Masashi Sugiyama

  • Density Ratio Estimation in Machine Learning

    Masashi Sugiyama;Taiji Suzuki;Takafumi Kanamori

  • Deep learning, reinforcement learning, and world models

    Unknown

  • A Least-squares Approach to Direct Importance Estimation

    Takafumi Kanamori;Shohei Hido;Masashi Sugiyama

  • Local Fisher discriminant analysis for supervised dimensionality reduction

    Masashi Sugiyama

  • How does disagreement help generalization against label corruption

    Xingrui Yu;Bo Han;Jiangchao Yao;Gang Niu

  • Semi-supervised local Fisher discriminant analysis for dimensionality reduction

    Masashi Sugiyama;Tsuyoshi Idé;Shinichi Nakajima;Jun Sese

  • High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

    Makoto Yamada;Wittawat Jitkrittum;Leonid Sigal;Eric P. Xing

  • Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

    Masashi Sugiyama;Motoaki Kawanabe

  • Positive-Unlabeled Learning with Non-Negative Risk Estimator

    Ryuichi Kiryo;Gang Niu;Marthinus Christoffel du Plessis;Masashi Sugiyama

  • Learning discrete representations via information maximizing self-augmented training

    Weihua Hu;Takeru Miyato;Seiya Tokui;Eiichi Matsumoto

  • Analysis of Learning from Positive and Unlabeled Data

    Marthinus C du Plessis;Gang Niu;Masashi Sugiyama

  • Active Learning in Recommender Systems

    Neil Rubens;Dain Kaplan;Masashi Sugiyama

  • Convex Formulation for Learning from Positive and Unlabeled Data

    Marthinus Du Plessis;Gang Niu;Masashi Sugiyama

Frequent Co-Authors

Taiji Suzuki
Taiji Suzuki University of Tokyo
Motoaki Kawanabe
Motoaki Kawanabe Advanced Telecommunications Research Institute International
Hisashi Kashima
Hisashi Kashima Kyoto University
Ryota Tomioka
Ryota Tomioka Microsoft (United States)
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research
Neil D. Lawrence
Neil D. Lawrence University of Cambridge
Tongliang Liu
Tongliang Liu University of Sydney
Alexandra J. Golby
Alexandra J. Golby Brigham and Women's Hospital

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