Kenji Fukumizu

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Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Gert LanckrietProfessor, UCSDVerified email at eng.ucsd.edu
Le SongBiomap, Mohamed bin Zayed University of Artificial IntelligenceVerified email at biomap.com
Alex SmolaBoson AIVerified email at smola.org
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Dino SejdinovicProfessor, University of AdelaideVerified email at adelaide.edu.au
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Amari S*Verified email at brain.riken.jp
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Motonobu KanagawaEURECOMVerified email at eurecom.fr
Choon Hui TeoAmazonVerified email at amazon.com
Md. Ashad AlamInstructor of Statistics and Data Science, Tulane University School of Medicine, USAVerified email at tulane.edu
Francesco DinuzzoMetaVerified email at fb.com
Song LiuSenior Lecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Marco CuturiApple / CREST-ENSAE.Verified email at apple.com
Kazuo HaraYamagata UniversityVerified email at sci.kj.yamagata-u.ac.jp
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Massimiliano PontilIstituto Italiano di Tecnologia & University College LondonVerified email at iit.it

Kenji Fukumizu

The Institute of Statistical Mathematics

Verified email at ism.ac.jp - Homepage

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A kernel statistical test of independence A Gretton, K Fukumizu, C Teo, L Song, B Schölkopf, A Smola Advances in neural information processing systems 20, 2007	938	2007
Hilbert space embeddings and metrics on probability measures BK Sriperumbudur, A Gretton, K Fukumizu, B Schölkopf, GRG Lanckriet The Journal of Machine Learning Research 11, 1517-1561, 2010	793	2010
Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces K Fukumizu, FR Bach, MI Jordan Journal of Machine Learning Research 5 (Jan), 73-99, 2004	710	2004
Kernel mean embedding of distributions: A review and beyond K Muandet, K Fukumizu, B Sriperumbudur, B Schölkopf Foundations and Trends® in Machine Learning 10 (1-2), 1-141, 2017	694	2017
Optimal kernel choice for large-scale two-sample tests A Gretton, D Sejdinovic, H Strathmann, S Balakrishnan, M Pontil, ... Advances in neural information processing systems 25, 2012	689	2012
Equivalence of distance-based and RKHS-based statistics in hypothesis testing D Sejdinovic, B Sriperumbudur, A Gretton, K Fukumizu The annals of statistics, 2263-2291, 2013	676	2013
Kernel measures of conditional dependence K Fukumizu, A Gretton, X Sun, B Schölkopf Advances in neural information processing systems 20, 2007	669	2007
Universality, Characteristic Kernels and RKHS Embedding of Measures. BK Sriperumbudur, K Fukumizu, GRG Lanckriet Journal of Machine Learning Research 12 (7), 2011	486	2011
Kernel dimension reduction in regression K Fukumizu, FR Bach, MI Jordan	389	2009
Hilbert space embeddings of conditional distributions with applications to dynamical systems L Song, J Huang, A Smola, K Fukumizu Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	360	2009
On the empirical estimation of integral probability metrics BK Sriperumbudur, K Fukumizu, A Gretton, B Schölkopf, GRG Lanckriet	329	2012
Statistical consistency of kernel canonical correlation analysis. K Fukumizu, FR Bach, A Gretton Journal of Machine Learning Research 8 (2), 2007	318	2007
Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models L Song, K Fukumizu, A Gretton IEEE Signal Processing Magazine 30 (4), 98-111, 2013	256	2013
Kernel choice and classifiability for RKHS embeddings of probability distributions K Fukumizu, A Gretton, G Lanckriet, B Schölkopf, BK Sriperumbudur Advances in neural information processing systems 22, 2009	255	2009
Adaptive method of realizing natural gradient learning for multilayer perceptrons S Amari, H Park, K Fukumizu Neural computation 12 (6), 1399-1409, 2000	254	2000
A fast, consistent kernel two-sample test A Gretton, K Fukumizu, Z Harchaoui, BK Sriperumbudur Advances in neural information processing systems 22, 2009	253	2009
Local minima and plateaus in hierarchical structures of multilayer perceptrons K Fukumizu, S Amari Neural networks 13 (3), 317-327, 2000	239	2000
Learning from distributions via support measure machines K Muandet, K Fukumizu, F Dinuzzo, B Schölkopf Advances in neural information processing systems 25, 2012	219	2012
Adaptive natural gradient learning algorithms for various stochastic models H Park, SI Amari, K Fukumizu Neural Networks 13 (7), 755-764, 2000	218	2000
Persistence weighted Gaussian kernel for topological data analysis G Kusano, Y Hiraoka, K Fukumizu International conference on machine learning, 2004-2013, 2016	217	2016

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