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Kenji Fukumizu's Home Page

English/Japanese

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[画像:Self_portrait] Kenji Fukumizu

The Institute of Statistical Mathematics
Professor, Department of Mathematical Analysis and Statistical Inference
Director, Research Center for Statistical Machine Learning

Professor, Department of Statistical Science, Graduate University of Advanced Studies (SOKENDAI)

10-3 Midori-cho, Tachikawa, Tokyo 190-8562 Japan
E-mail: 'fukumizu' ATT 'ism' DOTT 'ac' DOTT jp
Room: D606. Access to ISM

Intensive Course "Machine Learning Approach to Toplogical Data Analysis" at Tokyo Metropolitan University , 2018. Info.

Variational Learning on Aggregate Outputs with Gaussian Processes,
H.C. Law, D. Sejdinovic, E. Cameron, T. Lucas, S. Flaxman, K. Battle, K. Fukumizu, has been accepted in NIPS 2018 (paper)

Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
S. Yokoi, S.e Kobayashi, K. Fukumizu, J. Suzuki and K. Inui, has been accepted in EMNLP 2018. (arXiv version)

Kernel Recursive ABC: Point Estimation with Intractable Likelihood
by Kajihara, Yamazaki, Kanagawa, and Fukumizu, has been accepted by ICML 2018. paper

Post Selection Inference with Kernels
by Makoto Yamada, Yuta Umezu, Kenji Fukumizu, and Ichiro Takeuchi has been accepted by AISTATS 2018. paper

2018年2月19日-21 Workshop on Functional Inference and Machine Intelligence (FIMI) will be held at ISM, Tokyo. Please join.

NIPS 2017 Best Paper Award!!
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton. A Linear-Time Kernel Goodness-of-Fit Test.

K. Muandet, K. Fukumizu, B. Sriperumbudur and B.d Sch?lkopf (2017),
Kernel Mean Embedding of Distributions: A Review and Beyond,
Foundations and Trends? in Machine Learning: Vol. 10: No. 1-2, pp 1-141.
The first comprehensive review on kernel mean embedding methods!




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My research Interest:
I am interested in thoery and practice of learning with complex and structured data, and working from the viewpoints of statistics, mathematics and machine learning. More specific research projects include the following topics;

• Topological Data Analysis (TDA): A new methodoogy of data anlalysis for complex geometrical objects. TDA uses persistence homology, which expresses topological and geometrical information of data in an algebraic form.
• Kernel method (data analysis with positive definite kernels): Nonparametric data analysis with positive definite kernels and reproducing kernel Hilbert spaces. Expressing probabilistic knowledge using embedding of data in reproducing kernel Hilbert spaces, and its applications to extracting dependence, conditional dependence structure among variables. Inference of causal networks with these methods.
• Geometry of algorithms on graphs: Analysis of algorithms on graphs, such as belief propagation, with graph geometry, graph polynomial and so on.
• Singular statistical models: Statistical inference with parametric models with singularities. Nonstandard asymptotic behavior of the estimator around sigularities.

Welcome! I welcome students and collaborators interested in the above topics. Contact me by email.


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Publications


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Softwares


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Selected talks


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Courses

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Curriculum Vitae


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Lab Members


• Masaaki Imaizumi(JSPS Research Fellow)
• Jin Zhou (Project Researcher)
• Yuki Saito (Ph.D. Candidate, SOKENDAI)
• Daniel Andrade (Ph.D. Candidate, SOKENDAI)
• Kyoko Akatsuka, Shioko Fukuda (Secretary)

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Alumni


• Song Liu (University of Bristol, Lecturer)
• Motonobu Kanagawa (Max Planck Institute for Intelligent Systems)
• Yoshimasa Uematsu (University of Southern California)
• Takashi Arai (Shiga University, Assistant Professor)
• Momoko Hayamizu (The Institute of Statistical Mathematics, Assistant Professor)
• Shaogao Lv (Visiting Associate Professor, Southwestern University of Finance and Economic)
• Yusuke Aikawa (Master Degree, Tokyo Institute of Technology)
• Yu NIshiyama(Assistant Professor, The University of Electro-Communications)
• Md Ashad Alam (Hajee Mohammad Danesh Science & Technology University, Bangladesh)
• Jonathan Root (NSF Summer Program, June - August 2013, Boston Univ.)
• Revant Kumar (student intern, May 2013 - July 2013, IIT Guwahati.)
• Pierre Chiche (visiting student in 2012 from J.-P. Vert's lab.,Mines ParisTech.)
• Masamichi Sato (Dai Nippon Printing Co., Ltd.)
• Francesco Dinuzzo (Univ. Pavia, visitor till March 2011) (Amazon)
• Yusuke Watanabe (Amazon)
• Yuichi Shiraishi (Assistant Professor, Institute of Medical Science, Univ. Tokyo)
• Tomoko Ikeda-Fukazawa (Meiji University, Professor)
• Marco Cuturi (CREST - ENSAE, Universite Paris-Saclay, Professor)

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Academic Services


• Editorial services:
  • Chief Editor, Annals of the Institute of Statistical Mathematics (Springer)
  • Associate Editor, Neural Networks - 2011.12 (Elsevier).
  • Action Editor, Journal of Machine Learning Research. (MIT Press)
  • Editor, Foundations and Trends in Machine Learning. (Now Publishers)
• Organizers:
  • NIPS 2017, Program Commettee, Area Chair.
  • ICML 2017, Program Commettee, Area Chair.
  • NIPS 2015, Program Commettee, Area Chair.
  • IBIS 2015, Organizing Chair.
  • NIPS 2011, Program Commettee, Area Chair.
  • ACML 2011, Senior Program Committee.
  • ICML 2009, Program Committee, Area Chair.
  • NIPS 2010, Program Committee, Area Chair.
  • IBIS 2010, Co-organizer.
  And have worked as program committee for many conferences.

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Miscellaneous (in Japanese)


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