Arthur Gretton

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Alex SmolaBoson AIVerified email at smola.org
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Le SongBiomap, Mohamed bin Zayed University of Artificial IntelligenceVerified email at biomap.com
Malte J RaschSony AI (previously: IBM Research AI)Verified email at sony.com
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Michael ArbelInria - Univ. Grenoble AlpesVerified email at inria.fr
Danica J. SutherlandUniversity of British Columbia + AmiiVerified email at cs.ubc.ca
Nikos LogothetisVisiting Professor of Neuroscience, Stanford UniversityVerified email at stanford.edu
Olivier BousquetGoogleVerified email at m4x.org
Ralf HerbrichHasso Plattner InstituteVerified email at hpi.de
Heiko StrathmannResearch Scientist at DeepmindVerified email at deepmind.com
Gert LanckrietProfessor, UCSDVerified email at eng.ucsd.edu
Matthew BlaschkoKU LeuvenVerified email at esat.kuleuven.be
Zoltan SzaboProfessor, LSEVerified email at lse.ac.uk
Hans-Peter KriegelProfessor für Informatik, Ludwig-Maximilians-University MunichVerified email at dbs.ifi.lmu.de
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Massimiliano PontilIstituto Italiano di Tecnologia & University College LondonVerified email at iit.it

Arthur Gretton

Gatsby Computational Neuroscience Unit and Google Deepmind

Verified email at gatsby.ucl.ac.uk - Homepage

generative models causality hypothesis testing kernel methods machine learning


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A kernel two-sample test A Gretton, KM Borgwardt, MJ Rasch, B Schölkopf, A Smola The Journal of Machine Learning Research 13 (1), 723-773, 2012	5833	2012
A kernel method for the two-sample-problem A Gretton, K Borgwardt, M Rasch, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006	2679	2006
Correcting sample selection bias by unlabeled data J Huang, A Gretton, K Borgwardt, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006	2190	2006
Measuring statistical dependence with Hilbert-Schmidt norms A Gretton, O Bousquet, A Smola, B Schölkopf International conference on algorithmic learning theory, 63-77, 2005	2002	2005
Integrating structured biological data by kernel maximum mean discrepancy KM Borgwardt, A Gretton, MJ Rasch, HP Kriegel, B Schölkopf, AJ Smola Bioinformatics 22 (14), e49-e57, 2006	1740	2006
Demystifying mmd gans M Bińkowski, DJ Sutherland, M Arbel, A Gretton arXiv preprint arXiv:1801.01401, 2018	1482	2018
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	1073	2007
A Hilbert space embedding for distributions A Smola, A Gretton, L Song, B Schölkopf International conference on algorithmic learning theory, 13-31, 2007	1027	2007
Ranking on data manifolds D Zhou, J Weston, A Gretton, O Bousquet, B Schölkopf Advances in neural information processing systems 16, 2003	989	2003
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	869	2010
Covariate shift by kernel mean matching A Gretton, A Smola, J Huang, M Schmittfull, K Borgwardt, B Schölkopf	841	2008
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	802	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	767	2013
Kernel measures of conditional dependence K Fukumizu, A Gretton, X Sun, B Schölkopf Advances in neural information processing systems 20, 2007	738	2007
Feature Selection via Dependence Maximization. L Song, A Smola, A Gretton, J Bedo, K Borgwardt Journal of Machine Learning Research 13 (5), 2012	495	2012
Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information A Belitski, A Gretton, C Magri, Y Murayama, MA Montemurro, ... Journal of Neuroscience 28 (22), 5696-5709, 2008	481	2008
Kernel methods for measuring independence. A Gretton, R Herbrich, A Smola, O Bousquet, B Schölkopf, A Hyvärinen Journal of Machine Learning Research 6 (12), 2005	472	2005
Supervised feature selection via dependence estimation L Song, A Smola, A Gretton, KM Borgwardt, J Bedo Proceedings of the 24th international conference on Machine learning, 823-830, 2007	466	2007
On the empirical estimation of integral probability metrics BK Sriperumbudur, K Fukumizu, A Gretton, B Schölkopf, GRG Lanckriet	359	2012
A kernel test of goodness of fit K Chwialkowski, H Strathmann, A Gretton International conference on machine learning, 2606-2615, 2016	353	2016

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