Joaquin Quiñonero Candela

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Coauteurs

Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeAdresse e-mail validée de cam.ac.uk
Neil D. LawrenceUniversity of CambridgeAdresse e-mail validée de cl.cam.ac.uk
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoAdresse e-mail validée de k.u-tokyo.ac.jp
Ralf HerbrichZalandoAdresse e-mail validée de zalando.de
Thore GraepelResearch Scientist, Google DeepMind, and Professor of Computer Science, UCLAdresse e-mail validée de ucl.ac.uk
Roderick Murray-SmithProfessor of Computing Science, University of Glasgow, School of Computing ScienceAdresse e-mail validée de glasgow.ac.uk
Patrice SimardMicrosoft ResearchAdresse e-mail validée de microsoft.com
Denis CharlesMicrosoft ResearchAdresse e-mail validée de microsoft.com
Miguel Lázaro-GredillaDirector of research, Vicarious AIAdresse e-mail validée de vicarious.com
Jan LarsenTechnical University of Denmark, DTU ComputeAdresse e-mail validée de dtu.dk
Fabian SinzGroup Leader, University TübingenAdresse e-mail validée de uni-tuebingen.de
Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkAdresse e-mail validée de dtu.dk
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedAdresse e-mail validée de tuebingen.mpg.de
Ido DaganProfessor, Computer Science Department, Bar-Ilan UniversityAdresse e-mail validée de cs.biu.ac.il
Bernardo MagniniResearcher, Fondazone Bruno Kessler - FBK, Trento, ItalyAdresse e-mail validée de fbk.eu
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkAdresse e-mail validée de bio.ku.dk
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisAdresse e-mail validée de telecom-paris.fr
Leon BottouFacebook AI ResearchAdresse e-mail validée de bottou.org
Jonas PetersProfessor of Statistics, University of CopenhagenAdresse e-mail validée de math.ku.dk

Joaquin Quiñonero Candela

Engineering Director at Facebook

Adresse e-mail validée de fb.com - Page d'accueil

Machine Learning Bayesian Inference


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
A unifying view of sparse approximate Gaussian process regression J Quinonero-Candela, CE Rasmussen The Journal of Machine Learning Research 6, 1939-1959, 2005	1587	2005
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	917	2009
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009	852	2009
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	538	2010
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	506	2014
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	485*	2003
Counterfactual reasoning and learning systems: The example of computational advertising D Charles, M Chickering, P Simard Journal of Machine Learning Research 14, 2013	393	2013
Sparse spectrum Gaussian process regression M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ... Journal of Machine Learning Research 11 (Jun), 1865-1881, 2010	368	2010
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	259	2006
When training and test sets are different: characterizing learning transfer A Storkey Dataset shift in machine learning 30, 3-28, 2009	195	2009
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	166*	2003
Approximation methods for gaussian process regression J Quiñonero-Candela, CE Rasmussen, CKI Williams Large-scale kernel machines, 203-224, 2007	155	2007
Healing the relevance vector machine through augmentation CE Rasmussen, J Quiñonero-Candela Proceedings of the 22nd international conference on Machine learning, 689-696, 2005	97	2005
Event Prediction R Herbrich, T Graepel, O Zoeter, JQ Candela, P Trelford US Patent App. 11/835,985, 2009	91	2009
Learning with uncertainty-Gaussian processes and relevance vector machines J Quiñonero-Candela	73*	2004
Evaluating predictive uncertainty challenge J Quiñonero-Candela, C Rasmussen, F Sinz, O Bousquet, B Schölkopf Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006	72	2006
Event prediction in dynamic environments T Graepel, JQ Candela, TI Borchert, R Herbrich US Patent 8,417,650, 2013	70	2013
Learning depth from stereo F Sinz, J Quiñonero-Candela, G Bakır, C Rasmussen, M Franz Pattern Recognition, 245-252, 2004	60	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	56	2002
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	52	2003

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