Carl Edward Rasmussen

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Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Marc DeisenrothUniversity College LondonVerified email at ucl.ac.uk
Joaquin Quiñonero CandelaAI Technical Fellow at LinkedInVerified email at linkedin.com
Roderick Murray-SmithProfessor of Computing Science, University of Glasgow, School of Computing ScienceVerified email at glasgow.ac.uk
Hannes NickischPhilips ResearchVerified email at philips.com
Jan PetersProfessor for Intelligent Autonomous Systems at Technische Universität DarmstadtVerified email at ias.tu-darmstadt.de
Dilan GorurDeepMindVerified email at google.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichVerified email at tuebingen.mpg.de
Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Roger FrigolaMachine Learning at www.rogerfrigola.comVerified email at rogerfrigola.com
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Fabian SinzProfessor for Machine Learning, University GöttingenVerified email at cs.uni-goettingen.de
Miguel Lázaro-GredillaVP of research, Vicarious AIVerified email at vicarious.com
Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
Samy BengioSenior Director, AI and Machine Learning Research, AppleVerified email at apple.com
Cheng Soon OngData61, CSIRO, CanberraVerified email at anu.edu.au
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Volker TrespLudwig Maximilian University of Munich / München (LMU)Verified email at dbs.ifi.lmu.de

Carl Edward Rasmussen

Professor of Machine Learning, University of Cambridge

Verified email at cam.ac.uk - Homepage

Gaussian processes Bayesian inference machine learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Gaussian Processes for Machine Learning CE Rasmussen, CKI Williams the MIT Press, 2006	26437	2006
Gaussian processes in machine learning CE Rasmussen Lecture notes in computer science 3176, 63-71, 2004	3786	2004
The infinite hidden Markov model MJ Beal, Z Ghahramani, CE Rasmussen Advances in neural information processing systems 14, 577-584, 2002	2597*	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	2231	2005
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	1696	2011
The infinite Gaussian mixture model CE Rasmussen Advances in neural information processing systems 12, 554-560, 2000	1685	2000
Gaussian processes for regression CKI Williams, CE Rasmussen Advances in Neural Processing Systems 8, 514 - 520, 1996	1581	1996
Gaussian processes for machine learning (GPML) toolbox CE Rasmussen, H Nickisch The Journal of Machine Learning Research 11, 3011-3015, 2010	1109	2010
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	707	2015
Evaluation of Gaussian processes and other methods for non-linear regression CE Rasmussen University of Toronto, 1996	648	1996
Infinite mixtures of Gaussian process experts CE Rasmussen, Z Ghahramani Advances in neural information processing systems 14 2, 881-888, 2002	620	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	593*	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	525*	2002
Sparse spectrum Gaussian process regression M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ... The Journal of Machine Learning Research 11, 1865-1881, 2010	516	2010
Approximations for binary Gaussian process classification H Nickisch, CE Rasmussen Journal of Machine Learning Research 9, 2035-2078, 2008	440	2008
Warped Gaussian processes E Snelson, CE Rasmussen, Z Ghahramani Advances in neural information processing systems 16, 337-344, 2004	416	2004
Assessing approximate inference for binary Gaussian process classification M Kuss, CE Rasmussen The Journal of Machine Learning Research 6, 1679-1704, 2005	389	2005
Derivative observations in Gaussian process models of dynamic systems E Solak, R Murray-Smith, WE Leithead, DJ Leith, CE Rasmussen MIT Press, 2003	374	2003
Occam's razor CE Rasmussen, Z Ghahramani Advances in neural information processing systems, 294-300, 2001	360	2001
Bayesian monte Carlo CE Rasmussen, Z Ghahramani Advances in neural information processing systems 15, 489-496, 2003	349*	2003

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