Matthias Seeger

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Cédric ArchambeauDirector of AI at Helsing. Ex Principal Applied Scientist at AWS.Adresse 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
Rodolphe JENATTONCTO at BioptimusAdresse e-mail validée de bioptimus.com
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
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIAdresse e-mail validée de ias.tu-darmstadt.de
Neil D. LawrenceUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
Aaron KleinScaDS.AIAdresse e-mail validée de amazon.com
David SalinasUniversity of FreiburgAdresse e-mail validée de ens-lyon.org
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
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
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtAdresse e-mail validée de eins.tu-darmstadt.de
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	3177	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	2933	2009
Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004	1499	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	1102	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	913	2018
Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Advances in neural information processing systems 15, 2002	759	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	675	2003
PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002	447	2002
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	414	2009
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	387	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	360	2008
Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005	325	2005
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003	261	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	251	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	244	2000
Expectation propagation for exponential families M Seeger	211	2005
Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008	209	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	192	2010
Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in neural information processing systems 31, 2018	186	2018

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