Matthias Seeger

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Cédric ArchambeauDirector of AI at Helsing. Ex Principal Applied Scientist at AWS.Email yang diverifikasi di helsing.ai
Sham M KakadeHarvard UniversityEmail yang diverifikasi di seas.harvard.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichEmail yang diverifikasi di inf.ethz.ch
Rodolphe JENATTONCTO at BioptimusEmail yang diverifikasi di bioptimus.com
Hannes NickischPhilips ResearchEmail yang diverifikasi di philips.com
Chris WilliamsProfessor of Machine Learning, University of EdinburghEmail yang diverifikasi di inf.ed.ac.uk
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIEmail yang diverifikasi di ias.tu-darmstadt.de
Neil D. LawrenceUniversity of CambridgeEmail yang diverifikasi di cl.cam.ac.uk
Aaron KleinScaDS.AIEmail yang diverifikasi di amazon.com
David SalinasUniversity of FreiburgEmail yang diverifikasi di ens-lyon.org
Ralf HerbrichHasso Plattner InstituteEmail yang diverifikasi di hpi.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail yang diverifikasi di tuebingen.mpg.de
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail yang diverifikasi di cs.berkeley.edu
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindEmail yang diverifikasi di stats.ox.ac.uk
Andrew NgStanford UniversityEmail yang diverifikasi di cs.stanford.edu
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtEmail yang diverifikasi di eins.tu-darmstadt.de
Martin WistubaAmazon Web ServicesEmail yang diverifikasi di ismll.de

Ikuti

Matthias Seeger

Principal Applied Scientist, Amazon, Berlin

Email yang diverifikasi di amazon.de - Beranda

Machine Learning Bayesian Statistics Bayesian Optimization


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	3176	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	1494	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	1101	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	912	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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