World's Best Scientists 2026 revealed!
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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
46
Citations
10669
World Ranking
6755
National Ranking
97

Research.com Recognitions

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

Overview

Kenji Fukumizu is affiliated with The Institute of Statistical Mathematics in Japan. Their research primarily focuses on computer science, with a significant emphasis on artificial intelligence, statistics and probability, computer vision and pattern recognition, computational theory and mathematics, and materials chemistry.

The scientist's work covers several main topics, including domain adaptation and few-shot learning, topological and geometric data analysis, statistical methods and inference, machine learning and data classification, generative adversarial networks and image synthesis, Gaussian processes and Bayesian inference, and machine learning applications in materials science.

Fukumizu has published extensively in various venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • npj Computational Materials
  • IEEE Access
  • Pattern Recognition Letters

Co-authorship has been a notable aspect of their work. Frequent collaborators include:

  • Shunya Minami
  • Masanori Koyama
  • Pengzhou Wu
  • Tam Le
  • Truyen Nguyen

Selected recent papers by Kenji Fukumizu include:

  • "Smoothness and Stability in GANs," 2020, arXiv (Cornell University)
  • "ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables," 2022, IEEE Access
  • "Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces," 2020, arXiv (Cornell University)
  • "Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method," 2020, arXiv (Cornell University)
  • "Meta Learning for Causal Direction," 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

  • A Kernel Statistical Test of Independence

    Arthur Gretton;Kenji Fukumizu;Choon H. Teo;Le Song

  • Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

    Kenji Fukumizu;Francis R. Bach;Michael I. Jordan

  • Hilbert Space Embeddings and Metrics on Probability Measures

    Bharath K. Sriperumbudur;Arthur Gretton;Kenji Fukumizu;Bernhard Schölkopf

  • Kernel Mean Embedding of Distributions: A Review and Beyond

    Krikamol Muandet;Kenji Fukumizu;Bharath K. Sriperumbudur;Bernhard Schölkopf

  • Optimal kernel choice for large-scale two-sample tests

    Arthur Gretton;Dino Sejdinovic;Heiko Strathmann;Sivaraman Balakrishnan

  • Kernel Measures of Conditional Dependence

    Kenji Fukumizu;Arthur Gretton;Xiaohai Sun;Bernhard Schölkopf

  • Universality, Characteristic Kernels and RKHS Embedding of Measures

    Bharath K. Sriperumbudur;Kenji Fukumizu;Gert R. G. Lanckriet

  • Hilbert space embeddings of conditional distributions with applications to dynamical systems

    Le Song;Jonathan Huang;Alex Smola;Kenji Fukumizu

  • Statistical Consistency of Kernel Canonical Correlation Analysis

    Kenji Fukumizu;Francis R. Bach;Arthur Gretton

  • On the empirical estimation of integral probability metrics

    Bharath K. Sriperumbudur;Kenji Fukumizu;Arthur Gretton;Bernhard Schoelkopf

  • Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons

    Shun-Ichi Amari;Hyeyoung Park;Kenji Fukumizu

  • Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models

    Le Song;K. Fukumizu;A. Gretton

  • Local minima and plateaus in hierarchical structures of multilayer perceptions

    K. Fukumizu;S. Amari

  • A Fast, Consistent Kernel Two-Sample Test

    Arthur Gretton;Kenji Fukumizu;Zaïd Harchaoui;Bharath K. Sriperumbudur

  • Adaptive natural gradient learning algorithms for various stochastic models

    H. Park;S.-I. Amari;K. Fukumizu

  • Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

    Kenji Fukumizu;Arthur Gretton;Gert R. Lanckriet;Bernhard Schölkopf

  • Injective hilbert space embeddings of probability measures

    Bharath K. Sriperumbudur;Arthur Gretton;Kenji Fukumizu;Gert R. G. Lanckriet

  • Learning from Distributions via Support Measure Machines

    Krikamol Muandet;Kenji Fukumizu;Francesco Dinuzzo;Bernhard Schölkopf

  • Persistence weighted Gaussian kernel for topological data analysis

    Genki Kusano;Kenji Fukumizu;Yasuaki Hiraoka

  • Kernel Bayes' rule: Bayesian inference with positive definite kernels

    Kenji Fukumizu;Le Song;Arthur Gretton

  • On integral probability metrics, φ-divergences and binary classification

    Bharath K. Sriperumbudur;Kenji Fukumizu;Arthur Gretton;Bernhard Schölkopf

  • Density Estimation in Infinite Dimensional Exponential Families

    Bharath K. Sriperumbudur;Kenji Fukumizu;Arthur Gretton;Aapo Hyvärinen

Frequent Co-Authors

Arthur Gretton
Arthur Gretton University College London
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Taiji Suzuki
Taiji Suzuki University of Tokyo
Gert R. G. Lanckriet
Gert R. G. Lanckriet University of California, San Diego
Francis Bach
Francis Bach École Normale Supérieure
Le Song
Le Song Mohamed bin Zayed University of Artificial Intelligence
Shun-ichi Amari
Shun-ichi Amari RIKEN Center for Brain Science
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Marco Cuturi
Marco Cuturi École Nationale de la Statistique et de l'Administration Économique

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