Carl Edward Rasmussen

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Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresse e-mail validée de eng.cam.ac.uk
Marc DeisenrothUniversity College LondonAdresse e-mail validée de ucl.ac.uk
Joaquin Quiñonero CandelaSenior Fellow, Harvard Belfer CenterAdresse e-mail validée de hks.harvard.edu
Roderick Murray-SmithProfessor of Computing Science, University of Glasgow, School of Computing ScienceAdresse e-mail validée de glasgow.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Hannes NickischSenior Scientist at Philips ResearchAdresse e-mail validée de philips.com
Jan PetersProfessor for Intelligent Autonomous Systems at Technische Universität DarmstadtAdresse e-mail validée de ias.tu-darmstadt.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedAdresse e-mail validée de tuebingen.mpg.de
Dilan GorurDeepMindAdresse e-mail validée de google.com
Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkAdresse e-mail validée de dtu.dk
Roger FrigolaMachine Learning at www.rogerfrigola.comAdresse e-mail validée de rogerfrigola.com
Gunnar RätschProfessor, ETH ZürichAdresse e-mail validée de inf.ethz.ch
Fabian SinzProfessor for Machine Learning, University GöttingenAdresse e-mail validée de cs.uni-goettingen.de
Miguel Lázaro-GredillaVP of research, Vicarious AIAdresse e-mail validée de vicarious.com
Robert C. WilliamsonUniversity of TübingenAdresse e-mail validée de uni-tuebingen.de
Samy BengioSenior Director, AI and Machine Learning Research, AppleAdresse e-mail validée de apple.com
Cheng Soon OngData61, CSIRO, CanberraAdresse e-mail validée de anu.edu.au
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleAdresse e-mail validée de cs.toronto.edu
Volker TrespLudwig Maximilian University of Munich / München (LMU) and Siemens AGAdresse e-mail validée de dbs.ifi.lmu.de

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Carl Edward Rasmussen

Professor of Machine Learning, University of Cambridge

Adresse e-mail validée de cam.ac.uk - Page d'accueil

Bayesian inference machine learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Gaussian processes in machine learning CE Rasmussen Advanced Lectures on Machine Learning, 63-71, 2004	25214	2004
Gaussian Processes for Machine Learning CE Rasmussen, CKI Williams the MIT Press, 2006	25168	2006
The infinite hidden Markov model MJ Beal, Z Ghahramani, CE Rasmussen Advances in neural information processing systems 14, 577-584, 2002	2461*	2002
A unifying view of sparse approximate Gaussian process regression J Quiñonero-Candela, CE Rasmussen The Journal of Machine Learning Research 6, 1939-1959, 2005	1906	2005
The infinite Gaussian mixture model CE Rasmussen Advances in neural information processing systems 12, 554-560, 2000	1525	2000
PILCO: A model-based and data-efficient approach to policy search M Deisenroth, CE Rasmussen Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011	1432	2011
Gaussian processes for regression CKI Williams, CE Rasmussen Advances in Neural Processing Systems 8, 514 - 520, 1996	1404	1996
Gaussian processes for machine learning (GPML) toolbox CE Rasmussen, H Nickisch The Journal of Machine Learning Research 11, 3011-3015, 2010	975	2010
Evaluation of Gaussian processes and other methods for non-linear regression CE Rasmussen University of Toronto, 1996	609	1996
Gaussian Processes for Data-Efficient Learning in Robotics and Control M Deisenroth, D Fox, C Rasmussen IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 408-423, 2015	597	2015
Infinite mixtures of Gaussian process experts CE Rasmussen, Z Ghahramani Advances in neural information processing systems 14 2, 881-888, 2002	564	2002
Gaussian Process priors with uncertain inputs - Application to multiple-step ahead time series forecasting A Girard, CE Rasmussen, J Quinonero-Candela, R Murray-Smith MIT Press, 2003	543*	2003
Gaussian process priors with uncertain inputs: Multiple-step ahead prediction A Girard, CE Rasmussen, R Murray-Smith Delovno porocilo DCS TR-2002-119, University of Glasgow, Glasgow, 2002	479*	2002
Sparse spectrum Gaussian process regression M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ... The Journal of Machine Learning Research 11, 1865-1881, 2010	432	2010
Approximations for binary Gaussian process classification H Nickisch, CE Rasmussen Journal of Machine Learning Research 9, 2035-2078, 2008	412	2008
Warped Gaussian processes E Snelson, CE Rasmussen, Z Ghahramani Advances in neural information processing systems 16, 337-344, 2004	370	2004
Assessing approximate inference for binary Gaussian process classification M Kuss, CE Rasmussen The Journal of Machine Learning Research 6, 1679-1704, 2005	368	2005
Derivative observations in Gaussian process models of dynamic systems E Solak, R Murray-Smith, WE Leithead, DJ Leith, CE Rasmussen MIT Press, 2003	337	2003
Gaussian Processes in Reinforcement Learning M Kuss, CE Rasmussen Advances in Neural Information Processing Systems, None, 2003	308	2003
Bayesian monte Carlo CE Rasmussen, Z Ghahramani Advances in neural information processing systems 15, 489-496, 2003	308*	2003

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