Matthias Seeger - Citas de Google Académico

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Jan PetersProfessor at Technische Universität Darmstadt and Researcher at MPI for Intelligent SystemsDirección de correo verificada de ias.tu-darmstadt.de
Sham M KakadeUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Hannes NickischSenior Scientist at Philips ResearchDirección de correo verificada de philips.com
Neil D. LawrenceUniversity of CambridgeDirección de correo verificada de cl.cam.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghDirección de correo verificada de inf.ed.ac.uk
Andreas KrauseProfessor of Computer Science, ETH ZurichDirección de correo verificada de inf.ethz.ch
Ralf HerbrichZalandoDirección de correo verificada de zalando.de
Cédric ArchambeauAmazon Web Services, BerlinDirección de correo verificada de amazon.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedDirección de correo verificada de tuebingen.mpg.de
Michael I. JordanProfessor of EECS and Professor of Statistics, University of California, BerkeleyDirección de correo verificada de cs.berkeley.edu
Rodolphe JENATTONSenior machine learning scientist at Google Brain, BerlinDirección de correo verificada de google.com
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindDirección de correo verificada de stats.ox.ac.uk
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtDirección de correo verificada de eins.tu-darmstadt.de
Andrew NgStanford UniversityDirección de correo verificada de cs.stanford.edu

Matthias Seeger

Principal Applied Scientist, Amazon, Berlin

Dirección de correo verificada de amazon.de - Página principal

Machine Learning Bayesian Statistics Bayesian Optimization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Using the Nyström method to speed up kernel machines CKI Williams, M Seeger Advances in neural information processing systems, 682-688, 2001	2081	2001
Gaussian process optimization in the bandit setting: No regret and experimental design N Srinivas, A Krause, SM Kakade, M Seeger arXiv preprint arXiv:0912.3995, 2009	1049	2009
Learning with labeled and unlabeled data M Seeger	667	2000
Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004	580	2004
Fast sparse Gaussian process methods: The informative vector machine R Herbrich, ND Lawrence, M Seeger Advances in neural information processing systems, 625-632, 2003	578	2003
Fast forward selection to speed up sparse Gaussian process regression M Seeger, C Williams, N Lawrence Artificial Intelligence and Statistics 9, 2003	432	2003
Information-theoretic regret bounds for gaussian process optimization in the bandit setting N Srinivas, A Krause, SM Kakade, MW Seeger IEEE Transactions on Information Theory 58 (5), 3250-3265, 2012	360	2012
Bayesian inference and optimal design for the sparse linear model MW Seeger Journal of Machine Learning Research 9 (Apr), 759-813, 2008	325	2008
Pac-bayesian generalisation error bounds for gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002	272	2002
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	251	2009
Semiparametric latent factor models M Seeger, YW Teh, M Jordan	224	2005
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003	203	2003
The effect of the input density distribution on kernel-based classifiers C Williams, M Seeger Proceedings of the 17th international conference on machine learning, 2000	202	2000
Local gaussian process regression for real time online model learning D Nguyen-Tuong, JR Peters, M Seeger Advances in neural information processing systems, 1193-1200, 2009	200	2009
Expectation propagation for exponential families M Seeger	165	2005
Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers M Seeger Advances in neural information processing systems, 603-609, 2000	155	2000
Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008	134	2008
Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design M Seeger, H Nickisch, R Pohmann, B Schölkopf Magnetic Resonance in Medicine: An Official Journal of the International …, 2010	124	2010
Fast gaussian process regression using kd-trees Y Shen, M Seeger, AY Ng Advances in neural information processing systems, 1225-1232, 2006	122	2006
Compressed sensing and Bayesian experimental design MW Seeger, H Nickisch Proceedings of the 25th international conference on Machine learning, 912-919, 2008	118	2008

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