Matthias Seeger

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Cédric ArchambeauDirector of AI at Helsing. Ex Principal Applied Scientist at AWS.Dirección de correo verificada de helsing.ai
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichDirección de correo verificada de inf.ethz.ch
Rodolphe JENATTONCTO at BioptimusDirección de correo verificada de bioptimus.com
Hannes NickischPhilips 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
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIDirección de correo verificada de ias.tu-darmstadt.de
Aaron KleinScaDS.AIDirección de correo verificada de amazon.com
Ralf HerbrichHasso Plattner InstituteDirección de correo verificada de hpi.de
David SalinasUniversity of FreiburgDirección de correo verificada de ens-lyon.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHDirección de correo verificada de tuebingen.mpg.de
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
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
Martin WistubaAmazon Web ServicesDirección de correo verificada de ismll.de

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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 Advances in neural information processing systems 13, 2000	3166	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	2894	2009
Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004	1458	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	1080	2012
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	892	2018
Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Advances in neural information processing systems 15, 2002	761	2002
Learning with labeled and unlabeled data M Seeger	742	2000
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	684	2003
PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002	443	2002
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	410	2009
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	386	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	352	2008
Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005	326	2005
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003	262	2003
Leep: A new measure to evaluate transferability of learned representations C Nguyen, T Hassner, M Seeger, C Archambeau International Conference on Machine Learning, 7294-7305, 2020	246	2020
The effect of the input density distribution on kernel-based classifiers C Williams, M Seeger Proceedings of the 17th international conference on machine learning, 1159-1166, 2000	242	2000
Expectation propagation for exponential families M Seeger	213	2005
Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008	207	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	190	2010
Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in neural information processing systems 31, 2018	185	2018

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