I am a researcher at Microsoft Research Cambridge in the Online Services and Advertising and Applied Games groups. My work is focussed on the application of large scale machine learning and probabilistic modelling techniques to modelling and predicting online user behaviour, and to games.
Before joining Microsoft Research Cambridge, I was a postdoctoral researcher at the Fraunhofer Institute in Berlin and at the Technical University of Berlin with Prof. Klaus-Robert Müller. Before that, I was a postdoctoral researcher at the Max Planck Institute for Biological Cybernetics in the beautiful town of Tübingen in Southern Germany, working with my PhD advisor Dr. Carl E. Rasmussen and with Prof. Bernhard Schölkopf
I received my PhD from the Technical University of Denmark, where I worked with Lars Kai Hansen, Jan Larsen and Ole Winter in the Intelligent Signal Processing group. Prior to that, I graduated as a Telecommunications Engineer at the Carlos III University of Madrid, where I worked with Prof. Anibal Figueiras-Vidal, who first introduced me to machine learning.
J. Quiñonero Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Dataset Shift in Machine Learning, MIT Press, 2009
Anton Schwaighofer, Joaquin Quinonero Candela, Thomas Borchert, Thore Graepel, and Ralf Herbrich, Scalable Clustering and Keyword Suggestion for Online Advertisements, in Proceedings of ADKDD 2009: 3rd Annual International Workshop on Data Mining and Audience Intelligence for Advertising, Association for Computing Machinery, Inc., 2009
Miguel Lazaro-Gredilla, Joaquin Quiñonero Candela, and Anibal Figueiras-Vidal, Sparse Spectral Sampling Gaussian Processes, no. MSR-TR-2007-152, November 2007
Joaquin Quiñonero Candela, Edward Snelson, and Oliver Williams, Sensible Priors for Sparse Bayesian Learning, no. MSR-TR-2007-121, September 2007
Joaquin Quiñonero Candela, Carl Edward Rasmussen, and Christopher K. I. Williams, Approximation Methods for Gaussian Process Regression, no. MSR-TR-2007-124, September 2007
Neil D. Lawrence, Anton Schwaighofer, and Joaquin Quiñonero Candela, Gaussian Processes in Practice, 2007
J. Quiñonero Candela, C. E. Rasmussen, and C. K. I. Williams, Approximation Methods for Gaussian Process Regression, in Large Scale Learning Machines, pp. 203–223, MIT Press, 2007
J. Quiñonero Candela and C. E. Rasmussen, A Unifying View of Sparse Approximate Gaussian Process Regression, in Journal of Machine Learning Research, pp. 1935–1959, 2006
N. D. Lawrence and J. Quiñonero Candela, Local Distance Preservation in the GP-LVM Through Back Constraints, in Proceedings of the 23rd International Conference on Machine Learning, 2006
J. Quiñonero Candela, I. Dagan, B. Magnini, and F. D'Alché, Machine Learning Challenges - Evaluating Predictive Uncertainty, Textual Entailment and Object Recognition Systems, vol. 3944, Springer Verlag, 2006
J. Quiñonero Candela, C. E. Rasmussen, F. Sinz, O. Bousquet, and B. Schölkopf, Evaluating Predictive Uncertainty Challenge, in Machine Learning Challenges - Evaluating Predictive Uncertainty, Textual Entailment and Object Recognition Systems, vol. 3944, pp. 1–27, Springer, 2006
C. E. Rasmussen and J. Quiñonero Candela, Healing the Relevance Vector Machine through Augmentation, in Proceedings of the 22nd International Conference on Machine Learning, 2005
A. Zien and J. Quiñonero Candela, Large Margin Non-linear Embedding, in Proceedings of the 22nd International Conference on Machine Learning, 2005
J. Quiñonero Candela and C. E. Rasmussen, Analysis of some methods for reduced rank Gaussian process regression, in Switching and Learning in Feedback Systems, vol. 3355, pp. 98-127, Springer Verlag, 2005
J. Quiñonero Candela, Learning with Uncertainty - Gaussian Processes and Relevance Vector Machines, 2004
F. Sinz, J. Quiñonero Candela, G. H. Bakir, C. E. Rasmussen, and M. O. Franz, Learning Depth from Stereo, in Proceedings of the 26th DAGM Symposium, 2004
A. Girard, C. E. Rasmussen, J. Quiñonero Candela, and R. Murray-Smith, Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting, in Advances in Neural Information Processing Systems 15, 2003
J. Quiñonero Candela and O. Winther, Incremental Gaussian Processes, in Advances in Neural Information Processing Systems 15, 2003
J. Quiñonero Candela, A. Girard, J. Larsen, and C. E. Rasmussen, Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting, in Proceedings of the International Conference on Acoustics, Speech and Signal Processing Conference, 2003
J. Quiñonero Candela and L. K. Hansen, Time Series Prediction Based on the Relevance Vector Machine with Adaptive Kernels, in Proceedings of the International Conference on Acoustics, Speech and Signal Processing Conference, 2002
