D-Index & Metrics Best Publications
Masashi Sugiyama

Masashi Sugiyama

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 63 Citations 19,020 669 World Ranking 1721 National Ranking 12

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Masashi Sugiyama mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Statistics. His Artificial intelligence study is mostly concerned with Semi-supervised learning, Benchmark, Kernel, Supervised learning and Active learning. Masashi Sugiyama usually deals with Pattern recognition and limits it to topics linked to Least squares and Covariate shift and Partial least squares regression.

In the field of Machine learning, his study on Unsupervised learning, Dimensionality reduction and Computational learning theory overlaps with subjects such as Memorization. His Algorithm research is multidisciplinary, incorporating perspectives in Subspace topology, Distribution, Parametric statistics and Model selection. His work on Density estimation, Cross-validation, Conditional probability distribution and Estimator as part of his general Statistics study is frequently connected to Imagination, thereby bridging the divide between different branches of science.

His most cited work include:

  • Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis (865 citations)
  • Dataset Shift in Machine Learning (725 citations)
  • Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation (540 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Algorithm and Mathematical optimization. His research ties Estimator and Artificial intelligence together. His study in the field of Unsupervised learning, Binary classification and Stability also crosses realms of Multi-task learning.

Masashi Sugiyama interconnects Regularization, Kernel and Cluster analysis in the investigation of issues within Pattern recognition. His Algorithm study incorporates themes from Subspace topology, Density estimation, Parametric statistics and Model selection. The concepts of his Mathematical optimization study are interwoven with issues in Applied mathematics and Reinforcement learning.

He most often published in these fields:

  • Artificial intelligence (49.41%)
  • Machine learning (26.48%)
  • Pattern recognition (22.09%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (49.41%)
  • Machine learning (26.48%)
  • Algorithm (20.67%)

In recent papers he was focusing on the following fields of study:

Masashi Sugiyama spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Pattern recognition and Binary classification. His studies in Class, Deep learning, Classifier, Benchmark and Robustness are all subfields of Artificial intelligence research. His work in Machine learning tackles topics such as Estimator which are related to areas like Supervised learning.

His Algorithm study also includes

  • Upper and lower bounds together with Maximization,
  • Stochastic matrix which connect with Noise. His study in Pattern recognition is interdisciplinary in nature, drawing from both Data point and Pairwise comparison. His Binary classification research incorporates themes from Empirical risk minimization, Prior probability, Classifier and Metric.

Between 2017 and 2021, his most popular works were:

  • Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels (414 citations)
  • Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks (91 citations)
  • Masking: A New Perspective of Noisy Supervision (82 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

Artificial intelligence, Machine learning, Robustness, Training set and Algorithm are his primary areas of study. The concepts of his Artificial intelligence study are interwoven with issues in Generalization and Pattern recognition. His research integrates issues of Adversarial system, Imitation learning and Benchmark in his study of Machine learning.

His research on Robustness also deals with topics like

  • Deep neural networks which intersects with area such as Lipschitz continuity and Scalability,
  • Artificial neural network that connect with fields like Bayesian probability, Mathematical optimization, Applied mathematics and Transformation. His Training set research includes elements of Active learning and Overfitting. His Algorithm study combines topics from a wide range of disciplines, such as Stochastic matrix, Class, Classifier and Estimator.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Dataset Shift in Machine Learning

Joaquin Quionero-Candela;Masashi Sugiyama;Anton Schwaighofer;Neil D. Lawrence.
In: nonero-Candela, JQ and Sugiyama, M and Schwaighofer, A and Lawrence, N, (eds.) (pp. pp. 131-160). MIT Press: Cambridge, MA. (2008) (2009)

1446 Citations

Dataset Shift in Machine Learning

Joaquin Quionero-Candela;Masashi Sugiyama;Anton Schwaighofer;Neil D. Lawrence.
In: nonero-Candela, JQ and Sugiyama, M and Schwaighofer, A and Lawrence, N, (eds.) (pp. pp. 131-160). MIT Press: Cambridge, MA. (2008) (2009)

1446 Citations

Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

Masashi Sugiyama.
Journal of Machine Learning Research (2007)

1180 Citations

Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

Masashi Sugiyama.
Journal of Machine Learning Research (2007)

1180 Citations

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

Masashi Sugiyama;Shinichi Nakajima;Hisashi Kashima;Paul V. Buenau.
neural information processing systems (2007)

793 Citations

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

Masashi Sugiyama;Shinichi Nakajima;Hisashi Kashima;Paul V. Buenau.
neural information processing systems (2007)

793 Citations

Covariate Shift Adaptation by Importance Weighted Cross Validation

Masashi Sugiyama;Matthias Krauledat;Klaus-Robert Müller.
Journal of Machine Learning Research (2007)

768 Citations

Covariate Shift Adaptation by Importance Weighted Cross Validation

Masashi Sugiyama;Matthias Krauledat;Klaus-Robert Müller.
Journal of Machine Learning Research (2007)

768 Citations

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

Bo Han;Quanming Yao;Xingrui Yu;Gang Niu.
neural information processing systems (2018)

660 Citations

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

Bo Han;Quanming Yao;Xingrui Yu;Gang Niu.
neural information processing systems (2018)

660 Citations

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