Alex Smola

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
Mu LiAmazonVerified email at amazon.com
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
Amr AhmedSenior Staff Research Scientist, Google AIVerified email at google.com
Le SongGenBio AI, Mohamed bin Zayed University of Artificial IntelligenceVerified email at mbzuai.ac.ae
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Quoc V. LeResearch Scientist, GoogleVerified email at stanford.edu
vapnikProfessor of Columbia, Fellow of NEC Labs America, Verified email at nec-labs.com
John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Sebastian MikaVerified email at sebastianmika.info
Jonas MuellerCleanlabVerified email at csail.mit.edu
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Alexandros KaratzoglouGoogle DeepmindVerified email at google.com
Choon Hui TeoAmazonVerified email at amazon.com
Malte J RaschSony AI (previously: IBM Research AI)Verified email at sony.com

Alex Smola

Boson AI

Verified email at smola.org - Homepage

Machine Learning Deep Learning Systems


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Learning with kernels: Support vector machines, regularization, optimization, and beyond B Schölkopf, AJ Smola the MIT Press, 2002	25162*	2002
A tutorial on support vector regression AJ Smola, B Schölkopf Statistics and computing 14, 199-222, 2004	16150	2004
Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998	11208	1998
Estimating the support of a high-dimensional distribution B Schölkopf, JC Platt, J Shawe-Taylor, AJ Smola, RC Williamson Neural computation 13 (7), 1443-1471, 2001	7696	2001
Support vector regression machines H Drucker, CJ Burges, L Kaufman, A Smola, V Vapnik Advances in neural information processing systems 9, 1996	7434	1996
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	6428	2012
Hierarchical attention networks for document classification Z Yang, D Yang, C Dyer, X He, A Smola, E Hovy Proceedings of the 2016 conference of the North American chapter of the …, 2016	6263	2016
Support vector method for function approximation, regression estimation and signal processing V Vapnik, S Golowich, A Smola Advances in neural information processing systems 9, 1996	4472	1996
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3845	2000
Kernel principal component analysis B Schölkopf, A Smola, KR Müller International conference on artificial neural networks, 583-588, 1997	3601	1997
Support vector method for novelty detection B Schölkopf, RC Williamson, A Smola, J Shawe-Taylor, J Platt Advances in neural information processing systems 12, 1999	3308	1999
Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017	3157	2017
Kernel methods in machine learning T Hofmann, B Schölkopf, AJ Smola	3044	2008
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	2834	2006
Communication efficient distributed machine learning with the parameter server M Li, DG Andersen, A Smola, K Yu Advances in neural information processing systems 27, 2014	2815	2014
Advances in kernel methods: support vector learning B Schölkopf, CJC Burges, AJ Smola MIT press, 1999	2688	1999
A generalized representer theorem B Schölkopf, R Herbrich, AJ Smola International conference on computational learning theory, 416-426, 2001	2405	2001
Stacked attention networks for image question answering Z Yang, X He, J Gao, L Deng, A Smola Proceedings of the IEEE conference on computer vision and pattern …, 2016	2401	2016
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	2283	2006
kernlab-an S4 package for kernel methods in R A Karatzoglou, A Smola, K Hornik, A Zeileis Journal of statistical software 11, 1-20, 2004	2266	2004

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