Matthias Seeger

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Coauteurs

Cédric ArchambeauHelsing, BerlinAdresse e-mail validée de helsing.ai
Sham M KakadeHarvard UniversityAdresse e-mail validée de seas.harvard.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichAdresse e-mail validée de inf.ethz.ch
Hannes NickischPhilips ResearchAdresse e-mail validée de philips.com
Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Rodolphe JENATTONSenior machine learning scientist at Google Brain, BerlinAdresse e-mail validée de google.com
Neil D. LawrenceUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
Jan PetersProfessor for Intelligent Autonomous Systems at Technische Universität DarmstadtAdresse e-mail validée de ias.tu-darmstadt.de
David SalinasAmazonAdresse e-mail validée de ens-lyon.org
Aaron KleinAWS Research BerlinAdresse e-mail validée de amazon.com
Ralf HerbrichHasso Plattner InstituteAdresse e-mail validée de hpi.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHAdresse e-mail validée de tuebingen.mpg.de
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtAdresse e-mail validée de eins.tu-darmstadt.de
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindAdresse e-mail validée de stats.ox.ac.uk
Andrew NgStanford UniversityAdresse e-mail validée de cs.stanford.edu
Martin WistubaAmazon Web ServicesAdresse e-mail validée de ismll.de

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Matthias Seeger

Principal Applied Scientist, Amazon, Berlin

Adresse e-mail validée de amazon.de - Page d'accueil

Machine Learning Bayesian Statistics Bayesian Optimization


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	2877	2000
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	2280	2009
Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004	1051	2004
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	829	2012
Learning with labeled and unlabeled data M Seeger	727	2000
Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Advances in neural information processing systems 15, 2002	715	2002
Fast forward selection to speed up sparse Gaussian process regression MW Seeger, CKI Williams, ND Lawrence International Workshop on Artificial Intelligence and Statistics, 254-261, 2003	616	2003
Deep state space models for time series forecasting SS Rangapuram, MW Seeger, J Gasthaus, L Stella, Y Wang, ... Advances in neural information processing systems 31, 2018	576	2018
PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002	389	2002
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	372	2009
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	372	2007
Local Gaussian process regression for real time online model learning D Nguyen-Tuong, J Peters, M Seeger Advances in neural information processing systems 21, 2008	306	2008
Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005	305	2005
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003	240	2003
The effect of the input density distribution on kernel-based classifiers C Williams, M Seeger ICML'00 Proceedings of the Seventeenth International Conference on Machine …, 2000	227	2000
Expectation propagation for exponential families M Seeger	205	2005
Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008	184	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	177	2010
Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers M Seeger Advances in neural information processing systems 12, 1999	173	1999
Fast gaussian process regression using kd-trees Y Shen, M Seeger, A Ng Advances in neural information processing systems 18, 2005	156	2005

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