Scientific Publications

Practical Lessons from Predicting Clicks on Ads at Facebook
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela
ADKDD'14 Proceedings of the Eighth International Workshop on Data Mining for Online Advertising,pages 1--9, August 2014
[abstract] [pdf]

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
Journal of Machine Learning Research, vol. 14, pages 3207--3260, November 2013
[abstract] [pdf]

Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, and Aníbal R. Figueiras-Vidal
Journal of Machine Learning Research, vol. 11, pages 1865--1881, June 2010
[abstract] [pdf]

Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine
Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, and Ralf Herbrich
Proceedings of the 27th International Conference on Machine Learning 2010, pages 13--20, Haifa, Israel
[abstract] [pdf]

Dataset Shift in Machine Learning
Joaquin Quiñonero Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, editors.
MIT Press, Cambridge, MA, 2009
[MIT Press book website]

Scalable Clustering and Keyword Suggestion for Online Advertisements
Anton Schwaighofer, Joaquin Quiñonero Candela, Thomas Borchert, Thore Graepel, and Ralf Herbrich
Proceedings of ADKDD 2009: 3rd Annual International Workshop on Data Mining and Audience Intelligence for Advertising, pages 27--36. Copyright Association for Computing Machinery, Inc.
[abstract] [pdf]

Sparse Spectral Sampling Gaussian Processes
Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, and Aníbal R. Figueiras-Vidal
Microsoft Research Technical Report MSR-TR-2007-152, November 2007
[abstract] [pdf]

Sensible Priors for Sparse Bayesian Learning
Joaquin Quiñonero Candela, Edward Snelson, and Oliver Williams
Microsoft Research Technical Report MSR-TR-2007-121, September 2007
[abstract] [pdf]

Approximation Methods for Gaussian Process Regression
Joaquin Quiñonero Candela, Carl Edward Rasmussen, and Christopher K. I. Williams
Microsoft Research Technical Report MSR-TR-2007-124, September 2007
[abstract] [pdf]

Approximation Methods for Gaussian Process Regression
Joaquin Quiñonero Candela, Carl Edward Rasmussen, and Christopher K. I. Williams
In Large-Scale Kernel Machines, pages 203--224
(Edited by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston)
MIT Press, Cambridge, MA, 2007
[MIT Press book website - table of contents]

Gaussian Processes in Practice
Neil Lawrence, Anton Schwaighofer and Joaquin Quiñonero Candela, editors.
JMLR Workshop and Conference Proceedings, Volume 1, 2006
[JMLR proceedings website]

Local Distance Preservation in the GP-LVM Through Back Constraints
Neil Lawrence and Joaquin Quiñonero Candela
Proceedings of the 23rd International Conference on Machine Learning 2006, pages 512--520, Pittsburgh, PA
[pdf]

Machine Learning Challenges - Evaluating Predictive Uncertainty, Textual Entailment and Object Recognition Systems
Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, and Florence D'Alché-Buc, editors.
Lecture Notes in Computer Science, Springer, vol. 3944, 2006
[Book website]

Evaluating Predictive Uncertainty Challenge
Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian Sinz, Olivier Bousquet, and Bernhard Schölkopf
In Machine Learning Challenges, pages 1--27
(Edited by Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, and Florence D'Alché-Buc)
Lecture Notes in Computer Science, Springer, vol. 3944, 2006
[pdf]

A Unifying View of Sparse Approximate Gaussian Process Regression
Joaquin Quiñonero Candela and Carl Edward Rasmussen
Journal of Machine Learning Research, vol. 6, pages 1939--1959, December 2005
[abstract] [pdf]

Large Margin Non-linear Embedding
Alexander Zien and Joaquin Quiñonero Candela
Proceedings of the 22nd International Conference on Machine Learning 2005, pages 1060--1067, Bonn, Germany
[pdf]

Healing the Relevance Vector Machine through Augmentation
Carl Edward Rasmussen and Joaquin Quiñonero Candela
Proceedings of the 22nd International Conference on Machine Learning 2005, pages 689--696, Bonn, Germany
[pdf]

Analysis of some methods for reduced rank Gaussian process regression
Joaquin Quiñonero Candela and Carl Edward Rasmussen
In Switching and Learning in Feedback Systems, pages 98--127
(Edited by Roderick Murray-Smith and Robert Shorten)
Lecture Notes in Computer Science, Springer, vol. 3355, 2005
[pdf]

Learning Depth from Stereo
Fabian Sinz, Joaquin Quiñonero Candela, Gökhan H. Bakir, Carl Edward Rasmussen, and Matthis O. Franz
Proceedings of the 26th DAGM Symposium
Lecture Notes in Computer Science, Springer, vol. 3175, 2004
[pdf]

Learning with Uncertainty - Gaussian Processes and Relevance Vector Machines
Joaquin Quiñonero Candela
Technical University of Denmark, PhD Thesis number IMM-PHD-2004-135, 2004
[pdf]

Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting
Joaquin Quiñonero Candela, Agathe Girard, Jan Larsen and Carl Edward Rasmussen
Proceedings of the 2003 International Conference on Acoustics, Speech and Signal Processing, pages 701-704
[pdf]

Incremental Gaussian Processes
Joaquin Quiñonero Candela and Ole Winther
In Advances in Neural Information Processing Systems 15, pages 1001--1008, 2003
[pdf]

Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting
Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela and Roderick Murray-Smith
In Advances in Neural Information Processing Systems 15, pages 529--536, 2003
[pdf]

Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines - Application to Multiple-Step Ahead Time-Series Forecasting
Agathe Girard, Carl Edward Rasmussen and Joaquin Quiñonero Candela
Technical Report IMM-2003-18, Technical University of Denmark, 2003
[pdf]

Time Series Prediction Based on the Relevance Vector Machine with Adaptive Kernels
Joaquin Quiñonero Candela and Lars Kai Hansen
Proceedings of the 2002 International Conference on Acoustics, Speech and Signal Processing, pages 985-988
[pdf]