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Joaquin Quiñonero Candela
Joaquin Quiñonero Candela
OpenAI
Preverjeni e-poštni naslov na openai.com - Domača stran
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Leto
A unifying view of sparse approximate Gaussian process regression
J Quinonero-Candela, CE Rasmussen
Journal of machine learning research 6 (Dec), 1939-1959, 2005
26802005
Dataset shift in machine learning
J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
The MIT Press, 2009
26122009
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
11112014
Counterfactual reasoning and learning systems: The example of computational advertising
L Bottou, J Peters, J Quiñonero-Candela, DX Charles, DM Chickering, ...
The Journal of Machine Learning Research 14 (1), 3207-3260, 2013
8412013
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
7642010
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
690*2003
Sparse spectrum Gaussian process regression
M Lázaro-Gredilla, J Quiñonero-Candela, CE Rasmussen, ...
Journal of Machine Learning Research 11 (Jun), 1865-1881, 2010
6222010
When training and test sets are different: characterizing learning transfer
A Storkey
Dataset shift in machine learning 30 (3-28), 6, 2009
4752009
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
3112006
Approximation methods for gaussian process regression
J Quiñonero-Candela, CE Rasmussen, CKI Williams
Large-scale kernel machines, 203-224, 2007
2652007
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
200*2003
Dataset shift in machine learning
J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
The MIT Press, 2009
180*2009
Evaluating predictive uncertainty challenge
J Quiñonero-Candela, C Rasmussen, F Sinz, O Bousquet, B Schölkopf
Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006
1682006
Healing the relevance vector machine through augmentation
CE Rasmussen, J Quiñonero-Candela
Proceedings of the 22nd international conference on Machine learning, 689-696, 2005
1252005
Event prediction in dynamic environments
T Graepel, JQ Candela, TI Borchert, R Herbrich
US Patent 8,417,650, 2013
1202013
Event Prediction
R Herbrich, T Graepel, O Zoeter, JQ Candela, P Trelford
US Patent App. 11/835,985, 2009
1122009
Learning with uncertainty-Gaussian processes and relevance vector machines
J Quiñonero-Candela
1102004
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
752003
Learning depth from stereo
F Sinz, J Quiñonero-Candela, G Bakır, C Rasmussen, M Franz
Pattern Recognition, 245-252, 2004
662004
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
632002
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