Joaquin Quiñonero Candela

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Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Ralf HerbrichHasso Plattner InstituteVerified email at hpi.de
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCLVerified email at ucl.ac.uk
Leon BottouFacebook AI ResearchVerified email at bottou.org
Patrice SimardMicrosoft ResearchVerified email at microsoft.com
Jonas PetersProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
Denis CharlesMicrosoft ResearchVerified email at microsoft.com
Dipankar RayMicrosoftVerified email at microsoft.com
Roderick Murray-SmithProfessor of Computing Science, University of Glasgow, School of Computing ScienceVerified email at glasgow.ac.uk
Miguel Lázaro-GredillaResearch @ Google DeepMind. Ex VP of research @ Vicarious AI.Verified email at deepmind.com
Junfeng PanFacebook, Inc.Verified email at csail.mit.edu
Fabian SinzProfessor for Machine Learning, University GöttingenVerified email at cs.uni-goettingen.de
Jan LarsenTechnical University of Denmark, DTU ComputeVerified email at dtu.dk
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Ido DaganProfessor, Computer Science Department, Bar-Ilan UniversityVerified email at cs.biu.ac.il
Bernardo MagniniResearcher, Fondazone Bruno Kessler - FBK, Trento, ItalyVerified email at fbk.eu
Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk

Joaquin Quiñonero Candela

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A unifying view of sparse approximate Gaussian process regression J Quinonero-Candela, CE Rasmussen The Journal of Machine Learning Research 6, 1939-1959, 2005	2651	2005
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	2558	2009
Practical lessons from predicting clicks on ads at facebook X He, J Pan, O Jin, T Xu, B Liu, T Xu, Y Shi, A Atallah, R Herbrich, ... Proceedings of the eighth international workshop on data mining for online …, 2014	1108	2014
Counterfactual reasoning and learning systems: The example of computational advertising. L Bottou, J Peters, J Quiñonero-Candela, DX Charles, DM Chickering, ... Journal of Machine Learning Research 14 (11), 2013	841	2013
Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s Bing search engine T Graepel, J Quiñonero-Candela, T Borchert, R Herbrich Proc. 27th Internat. Conf. on Machine Learning. Morgan Kaufmann, San …, 2010	750	2010
Gaussian Process priors with uncertain inputs -- Application to multiple-step ahead time series forecasting A Girard, CE Rasmussen, J Quiñonero-Candela, R Murray-Smith MIT Press, 2003	682*	2003
Sparse spectrum Gaussian process regression M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ... Journal of Machine Learning Research 11 (Jun), 1865-1881, 2010	620	2010
When training and test sets are different: characterizing learning transfer A Storkey	474	2008
Local distance preservation in the GP-LVM through back constraints ND Lawrence, J Quinonero-Candela Proceedings of the 23rd international conference on Machine learning, 513-520, 2006	309	2006
Approximation methods for gaussian process regression J Quiñonero-Candela, CE Rasmussen, CKI Williams Large-scale kernel machines, 203-224, 2007	260	2007
Propagation of uncertainty in Bayesian kernel models-application to multiple-step ahead forecasting J Quiñonero-Candela, A Girard, J Larsen, CE Rasmussen Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03 …, 2003	199*	2003
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	179*	2009
Evaluating predictive uncertainty challenge J Quiñonero-Candela, C Rasmussen, F Sinz, O Bousquet, B Schölkopf Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006	165	2006
Healing the relevance vector machine through augmentation CE Rasmussen, J Quiñonero-Candela Proceedings of the 22nd international conference on Machine learning, 689-696, 2005	123	2005
Event prediction in dynamic environments T Graepel, JQ Candela, TI Borchert, R Herbrich US Patent 8,417,650, 2013	120	2013
Event Prediction R Herbrich, T Graepel, O Zoeter, JQ Candela, P Trelford US Patent App. 11/835,985, 2009	112	2009
Learning with uncertainty-Gaussian processes and relevance vector machines J Quiñonero-Candela	110	2004
Prediction at an uncertain input for Gaussian processes and relevance vector machines-application to multiple-step ahead time-series forecasting J Quinonero-Candela, A Girard, CE Rasmussen Technical University of Denmark, DTU: Informatics and Mathematical Modelling, 2003	74	2003
Learning depth from stereo F Sinz, J Quiñonero-Candela, G Bakır, C Rasmussen, M Franz Pattern Recognition, 245-252, 2004	65	2004
Time series prediction based on the relevance vector machine with adaptive kernels J Quiñonero-Candela, LK Hansen Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International …, 2002	64	2002

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