publications.bib

@INPROCEEDINGS{lawrence06bcgplvm,
  AUTHOR = {Neil D. Lawrence and Joaquin Quiñonero-Candela},
  TITLE = {Local distance preservation in the GP-LVM through back constraints},
  BOOKTITLE = {Proceedings of the International Conference in Machine Learning},
  YEAR = {2006},
  EDITOR = {W. Cohen and A. Moore},
  OPTVOLUME = {},
  OPTNUMBER = {},
  OPTSERIES = {},
  ADDRESS = {San Francisco, CA},
  PUBLISHER = {Morgan Kauffman},
  PAGES = {513-520},
  PDF = {papers/lawrence06bcgplvm.pdf}
}

@INPROCEEDINGS{quinonero06epuc,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Carl Edward Rasmussen and Fabian Sinz and Olivier Bousquet and Bernhard Sch{\"o}lkopf},
  TITLE = {Evaluating Predictive Uncertainty Challenge},
  BOOKTITLE = {Evaluating Predictive Uncertainty, Visual Object Categorization and Textual Entailment},
  YEAR = {2006},
  EDITOR = {Joaquin Qui{\~n}onero-Candela and Ido Dagan and Bernardo Magnini and Florence D'Alch{\'e}-Buc},
  VOLUME = {3944},
  PAGES = {1-27},
  PUBLISHER = {Springer},
  SERIES = {Lecture Notes in Computer Science},
  ADDRESS = {Heidelberg, Germany},
  PDF = {papers/quinonero06epuc.pdf}
}

@PROCEEDINGS{quinonero06lncs,
  TITLE = {Evaluating Predictive Uncertainty, Visual Object Categorization and Textual Entailment},
  YEAR = {2006},
  EDITOR = {Joaquin Qui{\~n}onero-Candela and Ido Dagan and Bernardo Magnini and Florence D'Alch{\'e}-Buc},
  VOLUME = {3944},
  PUBLISHER = {Springer},
  SERIES = {Lecture Notes in Computer Science},
  ADDRESS = {Heidelberg, Germany}
}

@ARTICLE{quinonero05unifying,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Carl Edward Rasmussen},
  TITLE = {A Unifying View of Sparse Approximate Gaussian Process Regression},
  JOURNAL = {Journal of Machine Learning Research},
  YEAR = {2005},
  VOLUME = {6},
  PAGES = {1935-1959},
  URL = {http://jmlr.csail.mit.edu/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf},
  ABSTRACT = {We provide a new unifying view, including all existing proper
probabilistic sparse approximations for Gaussian process regression. Our
approach relies on expressing the effective prior which the methods are
using. This allows new insights to be gained, and highlights the relationship
between existing methods. It also allows for a clear theoretically justified
ranking of the closeness of the known approximations to the corresponding full
GPs. Finally we point directly to designs of new better sparse approximations,
combining the best of the existing strategies, within attractive computational
constraints.}
}

@INPROCEEDINGS{rasmussen05healing,
  AUTHOR = {Carl Edward Rasmussen and Joaquin Qui{\~n}onero-Candela},
  TITLE = {{Healing the relevance vector machine by augmentation}},
  BOOKTITLE = {Proceedings of the 22nd International Conference on Machine
Learning},
  EDITOR = {L. De Raedt and S. Wrobel},
  YEAR = {2005},
  PAGES = {689-696},
  PDF = {papers/rasmussen05healing.pdf}
}

@INPROCEEDINGS{zien05large,
  AUTHOR = {Alexander Zien and Joaquin Qui{\~{n}}onero-Candela},
  TITLE = {{Large margin non-linear embedding}},
  BOOKTITLE = {Proceedings of the 22nd International Conference on Machine
Learning},
  EDITOR = {L. De Raedt and S. Wrobel},
  YEAR = {2005},
  PAGES = {1065-1072},
  PDF = {papers/zien05large.pdf}
}

@INPROCEEDINGS{quinonero05analysis,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Carl Edward Rasmussen},
  EDITOR = {Roderick Murray-Smith and Robert Shorten},
  BOOKTITLE = {{Switching and Learning in Feedback Systems}},
  TITLE = {Analysis of Some Methods for Reduced Rank Gaussian Process
Regression},
  PUBLISHER = {Springer},
  YEAR = {2005},
  VOLUME = {3355},
  SERIES = {Lecture Notes in Computer Science},
  ADDRESS = {Heidelberg, Germany},
  MONTH = {January},
  PAGES = {98-127},
  ABSTRACT = {While there is strong motivation for using Gaussian Processes
(GPs) due to their excellent performance in regression and classification
problems, their computational complexity makes them impractical when the size
of the training set exceeds a few thousand cases. This has motivated the recent
proliferation of a number of cost-effective approximations to GPs, both for
classification and for regression. In this paper we analyze one popular
approximation to GPs for regression: the reduced rank approximation. While
generally GPs are equivalent to infinite linear models, we show that Reduced
Rank Gaussian Processes (RRGPs) are equivalent to finite sparse linear
models. We also introduce the concept of degenerate GPs and show that they
correspond to inappropriate priors. We show how to modify the RRGP to prevent
it from being degenerate at test time. Training RRGPs consists both in learning
the covariance function hyperparameters and the support set. We propose a
method for learning hyperparameters for a given support set. We also review the
Sparse Greedy GP (SGGP) approximation (Smola and Bartlett, 2001), which is a
way of learning the support set for given hyperparameters based on
approximating the posterior. We propose an alternative method to the SGGP that
has better generalization capabilities. Finally we make experiments to compare
the different ways of training a RRGP. We provide some Matlab code for learning
RRGPs.},
  PDF = {papers/quinonero05analysis.pdf}
}

@PHDTHESIS{quinonero04thesis,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela },
  TITLE = {{Learning with Uncertainty -- {G}aussian Processes and {R}elevance
{V}ector {M}achines}},
  SCHOOL = {Technical University of Denmark},
  ADDRESS = {Lyngby, Denmark},
  YEAR = {2004},
  PDF = {papers/quinonero04thesis.pdf}
}

@INPROCEEDINGS{sinz04learning,
  AUTHOR = {F. Sinz and J. {Qui{\~n}onero-Candela} and G. H. Bakir and
C. E. Rasmussen and M.O. Franz},
  TITLE = {Learning Depth from Stereo},
  BOOKTITLE = {Proc.~26 DAGM Pattern Recognition Symposium},
  EDITOR = {Carl Edward Rasmussen and Henrich H. B{\"u}lthoff and Martin
A. Giese and Bernhard Sch{\"o}lkopf},
  PAGES = {245-252},
  YEAR = {2004},
  PUBLISHER = {Springer},
  ADDRESS = {Heidelberg, Germany},
  PDF = {papers/sinz04learning.pdf}
}

@INPROCEEDINGS{quinonero03incremental,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Ole Winther},
  TITLE = {Incremental Gaussian Processes},
  BOOKTITLE = {Advances in Neural Information Processing Systems 15},
  EDITOR = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
  PUBLISHER = {The MIT Press},
  ADDRESS = {Cambridge, MA},
  PAGES = {1001-1008},
  ALTURL = {http://books.nips.cc/papers/files/nips15/AA66.pdf},
  PDF = {papers/quinonero03incremental.pdf},
  YEAR = {2003}
}

@INPROCEEDINGS{girard02gaussian,
  AUTHOR = {Agathe Girard and Carl Edward Rasmussen and Joaquin
Qui{\~n}onero-Candela and Roderick Murray-Smith},
  TITLE = {Gaussian Process with Uncertain Inputs - Application to
Multiple-Step Ahead Time-Series Forecasting},
  BOOKTITLE = {Advances in Neural Information Processing Systems 15},
  PUBLISHER = {The MIT Press},
  ADDRESS = {Cambridge, MA},
  PAGES = {529--536},
  EDITOR = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
  ALTURL = {http://books.nips.cc/papers/files/nips15/AA06.pdf},
  PDF = {papers/girard03gaussian.pdf},
  YEAR = {2003}
}

@INPROCEEDINGS{quinonero03propagation,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Agathe Girard and Jan Larsen and
Carl Edward Rasmussen},
  TITLE = {Propagation of Uncertainty in Bayesian Kernels Models - Application
to Multiple-Step Ahead Forecasting},
  BOOKTITLE = {Proceedings of the International Conference on Acoustics, Speech
and Signal Processing},
  PUBLISHER = {IEEE},
  ADDRESS = {Piscataway, New Jersey},
  PAGES = {701-704},
  VOLUME = {2},
  YEAR = {2003},
  PDF = {papers/quinonero03propagation.pdf}
}

@TECHREPORT{quinonero02prediction,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Agathe Girard and Carl Edward
Rasmussen},
  TITLE = {Prediction at an Uncertain Input for Gaussian Processes and
Relevance Vector Machines - Application to Multiple-Step Ahead Time-Series
Forecasting},
  INSTITUTION = {Technical University of Denmark},
  YEAR = {2003},
  NUMBER = {IMM-2003-18},
  ADDRESS = {Lyngby, Denmark},
  PDF = {papers/quinonero03prediction.pdf}
}

@INPROCEEDINGS{quinonero02time,
  AUTHOR = {Joaquin Qui{\~n}onero-Candela and Lars Kai Hansen},
  TITLE = {Time Series Prediction Based on the Relevance Vector Machine with Adaptive Kernels},
  BOOKTITLE = {Proceedings of the International Conference on Acoustics,
Speech, and Signal Processing},
  PAGES = {985-988},
  VOLUME = {1},
  YEAR = {2002},
  PUBLISHER = {IEEE},
  ADDRESS = {Piscataway, New Jersey},
  PDF = {papers/quinonero02time.pdf}
}