Matthias Seeger

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Jan PetersProfessor for Intelligent Autonomous Systems at Technische Universität DarmstadtDirección de correo verificada de ias.tu-darmstadt.de
Sham M KakadeUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Cédric ArchambeauAmazon Web Services, BerlinDirección de correo verificada de amazon.com
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
Rodolphe JENATTONSenior machine learning scientist at Google Brain, BerlinDirección de correo verificada de google.com
Ralf HerbrichZalandoDirección de correo verificada de zalando.de
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedDirección de correo verificada de tuebingen.mpg.de
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtDirección de correo verificada de eins.tu-darmstadt.de
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindDirección de correo verificada de stats.ox.ac.uk
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 C Williams, M Seeger Proceedings of the 14th annual conference on neural information processing …, 2001	2387	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	1457	2009
Learning with labeled and unlabeled data M Seeger	704	2000
Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004	701	2004
Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Proceedings of the 16th annual conference on neural information processing …, 2003	643	2003
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	511	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	479	2012
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	358	2007
PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002	321	2002
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	293	2009
Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005	263	2005
Local gaussian process regression for real time online model learning and control D Nguyen-Tuong, J Peters, M Seeger Proceedings of the 21st International Conference on Neural Information …, 2008	238	2008
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	220	2000
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003	215	2003
Expectation propagation for exponential families M Seeger	186	2005
Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers M Seeger Proceedings of the 13th Annual Conference on Neural Information Processing …, 2000	165	2000
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, 7785-7794, 2018	159	2018
Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008	148	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	142	2010
Fast gaussian process regression using kd-trees Y Shen, A Ng, M Seeger Proceedings of the 19th Annual Conference on Neural Information Processing …, 2006	135	2006

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