Matthias Seeger

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Cédric ArchambeauHelsing, BerlinVerified email at helsing.ai
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Hannes NickischPhilips ResearchVerified email at philips.com
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Rodolphe JENATTONCTO at BioptimusVerified email at bioptimus.com
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIVerified email at ias.tu-darmstadt.de
Aaron KleinScaDS.AIVerified email at amazon.com
Ralf HerbrichHasso Plattner InstituteVerified email at hpi.de
David SalinasAmazonVerified email at ens-lyon.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtVerified email at eins.tu-darmstadt.de
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Andrew NgStanford UniversityVerified email at cs.stanford.edu
Martin WistubaAmazon Web ServicesVerified email at ismll.de

Matthias Seeger

Principal Applied Scientist, Amazon, Berlin

Verified email at amazon.de - Homepage

Machine Learning Bayesian Statistics Bayesian Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	3091	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	2723	2009
Gaussian processes for machine learning M Seeger International journal of neural systems 14 (02), 69-106, 2004	1331	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	1015	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	818	2018
Fast sparse Gaussian process methods: The informative vector machine N Lawrence, M Seeger, R Herbrich Advances in neural information processing systems 15, 2002	742	2002
Learning with labeled and unlabeled data M Seeger	736	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	664	2003
PAC-Bayesian generalisation error bounds for Gaussian process classification M Seeger Journal of machine learning research 3 (Oct), 233-269, 2002	431	2002
Model learning with local gaussian process regression D Nguyen-Tuong, M Seeger, J Peters Advanced Robotics 23 (15), 2015-2034, 2009	402	2009
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	383	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	340	2008
Semiparametric latent factor models YW Teh, M Seeger, MI Jordan International Workshop on Artificial Intelligence and Statistics, 333-340, 2005	320	2005
Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations M Seeger University of Edinburgh, 2003	257	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, 1159-1166, 2000	237	2000
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	226	2020
Expectation propagation for exponential families M Seeger	210	2005
Computed torque control with nonparametric regression models D Nguyen-Tuong, M Seeger, J Peters 2008 American Control Conference, 212-217, 2008	202	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	187	2010
Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers M Seeger Advances in neural information processing systems 12, 1999	180	1999

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