A unifying view of sparse approximate Gaussian process regression J Quinonero-Candela, CE Rasmussen The Journal of Machine Learning Research 6, 1939-1959, 2005 | 2503 | 2005 |
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009 | 2306 | 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 | 1059 | 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 | 794 | 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 | 733 | 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 | 649* | 2003 |
Sparse spectrum Gaussian process regression M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ... Journal of Machine Learning Research 11 (Jun), 1865-1881, 2010 | 583 | 2010 |
When training and test sets are different: characterizing learning transfer A Storkey | 439 | 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 | 301 | 2006 |
Approximation methods for gaussian process regression J Quiñonero-Candela, CE Rasmussen, CKI Williams Large-scale kernel machines, 203-224, 2007 | 247 | 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 | 194* | 2003 |
Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence The MIT Press, 2009 | 171* | 2009 |
Evaluating predictive uncertainty challenge J Quiñonero-Candela, C Rasmussen, F Sinz, O Bousquet, B Schölkopf Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006 | 153 | 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 | 119 | 2005 |
Event prediction in dynamic environments T Graepel, JQ Candela, TI Borchert, R Herbrich US Patent 8,417,650, 2013 | 112 | 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 | 108 | 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 | 71 | 2003 |
Learning depth from stereo F Sinz, J Quiñonero-Candela, G Bakır, C Rasmussen, M Franz Pattern Recognition, 245-252, 2004 | 64 | 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 | 63 | 2002 |