Carl Edward Rasmussen

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Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Marc DeisenrothUniversity College London and The Alan Turing InstituteVerified email at ucl.ac.uk
Joaquin Quiñonero CandelaOpenAIVerified email at openai.com
Roderick Murray-SmithProfessor of Computing Science, University of Glasgow, School of Computing ScienceVerified email at glasgow.ac.uk
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Hannes NickischPhilips ResearchVerified email at philips.com
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIVerified email at ias.tu-darmstadt.de
Dilan GorurDeepMindVerified email at google.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified 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. Computer Science, University of TorontoVerified email at cs.toronto.edu
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)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 MIT Press, 2006	34857	2006
The infinite hidden Markov model MJ Beal, Z Ghahramani, CE Rasmussen Advances in neural information processing systems 14, 577-584, 2002	2700*	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	2572	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	2029	2011
Gaussian processes for regression CKI Williams, CE Rasmussen Advances in Neural Processing Systems 8, 514 - 520, 1996	1924	1996
The infinite Gaussian mixture model CE Rasmussen Advances in neural information processing systems 12, 554-560, 2000	1847	2000
Gaussian processes for machine learning (GPML) toolbox CE Rasmussen, H Nickisch The Journal of Machine Learning Research 11, 3011-3015, 2010	1264	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	857	2015
Infinite mixtures of Gaussian process experts CE Rasmussen, Z Ghahramani Advances in neural information processing systems 14 2, 881-888, 2002	671	2002
Evaluation of Gaussian processes and other methods for non-linear regression CE Rasmussen University of Toronto, 1996	667	1996
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	662*	2003
Sparse spectrum Gaussian process regression M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ... The Journal of Machine Learning Research 11, 1865-1881, 2010	598	2010
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	589*	2002
Approximations for binary Gaussian process classification H Nickisch, CE Rasmussen Journal of Machine Learning Research 9, 2035-2078, 2008	496	2008
Warped Gaussian processes E Snelson, CE Rasmussen, Z Ghahramani Advances in neural information processing systems 16, 337-344, 2004	468	2004
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems 24, 226-234, 2012	427	2012
Derivative observations in Gaussian process models of dynamic systems E Solak, R Murray-Smith, WE Leithead, DJ Leith, CE Rasmussen MIT Press, 2003	423	2003
Assessing approximate inference for binary Gaussian process classification M Kuss, CE Rasmussen The Journal of Machine Learning Research 6, 1679-1704, 2005	418	2005
Occam's razor CE Rasmussen, Z Ghahramani Advances in neural information processing systems, 294-300, 2001	408	2001
Bayesian monte Carlo CE Rasmussen, Z Ghahramani Advances in neural information processing systems 15, 489-496, 2003	373*	2003

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