Introduction to Gaussian Processes

[1]  N. L. Johnson,et al.  Multivariate Analysis , 1958, Nature.

[2]  L. Tucker An inter-battery method of factor analysis , 1958 .

[3]  J. Kruskal Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[4]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[5]  N. Cressie,et al.  Universal cokriging under intrinsic coregionalization , 1994 .

[6]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[7]  D. Mackay,et al.  Bayesian neural networks and density networks , 1995 .

[8]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[9]  H. Wackernagle,et al.  Multivariate geostatistics: an introduction with applications , 1998 .

[10]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[11]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[12]  F. Woodward,et al.  Vegetation-climate feedbacks in a greenhouse world , 1998 .

[13]  Christopher K. I. Williams Computation with Infinite Neural Networks , 1998, Neural Computation.

[14]  David Barber,et al.  Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[16]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[17]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[18]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[19]  Roger Woodard,et al.  Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.

[20]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[21]  A. O'Hagan,et al.  Bayesian inference for the uncertainty distribution of computer model outputs , 2002 .

[22]  Michael I. Jordan,et al.  Sparse Gaussian Process Classification With Multiple Classes , 2004 .

[23]  Aaron Hertzmann,et al.  Style-based inverse kinematics , 2004, ACM Trans. Graph..

[24]  Neil D. Lawrence,et al.  Learning to learn with the informative vector machine , 2004, ICML.

[25]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[26]  Anton Schwaighofer,et al.  Learning Gaussian processes from multiple tasks , 2005, ICML.

[27]  Rajesh P. N. Rao,et al.  Learning Shared Latent Structure for Image Synthesis and Robotic Imitation , 2005, NIPS.

[28]  David J. Fleet,et al.  Priors for people tracking from small training sets , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  David J. Fleet,et al.  Gaussian Process Dynamical Models , 2005, NIPS.

[30]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[31]  Yee Whye Teh,et al.  Semiparametric latent factor models , 2005, AISTATS.

[32]  Joaquin Quiñonero Candela,et al.  Local distance preservation in the GP-LVM through back constraints , 2006, ICML.

[33]  William V. Baxter,et al.  Latent Doodle Space , 2006, Comput. Graph. Forum.

[34]  David J. Fleet,et al.  3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[35]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Colin Fyfe,et al.  A Gaussian process latent variable model formulation of canonical correlation analysis , 2006, ESANN.

[37]  Edwin V. Bonilla,et al.  Multi-task Gaussian Process Prediction , 2007, NIPS.

[38]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Human Pose Estimation , 2007, MLMI.

[39]  Andrew W. Fitzgibbon,et al.  The Joint Manifold Model for Semi-supervised Multi-valued Regression , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[40]  Trevor Darrell,et al.  Discriminative Gaussian process latent variable model for classification , 2007, ICML '07.

[41]  Neil D. Lawrence,et al.  Hierarchical Gaussian process latent variable models , 2007, ICML '07.

[42]  D. Higdon,et al.  Computer Model Calibration Using High-Dimensional Output , 2008 .

[43]  Neil D. Lawrence,et al.  Ambiguity Modeling in Latent Spaces , 2008, MLMI.

[44]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[45]  Samuel Kaski,et al.  Probabilistic approach to detecting dependencies between data sets , 2008, Neurocomputing.

[46]  D. di Bernardo,et al.  Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. , 2008, Genome research.

[47]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[48]  A. O'Hagan,et al.  Bayesian emulation of complex multi-output and dynamic computer models , 2010 .

[49]  Neil D. Lawrence,et al.  Bayesian Gaussian Process Latent Variable Model , 2010, AISTATS.

[50]  Guido Sanguinetti,et al.  Bayesian Multitask Classification With Gaussian Process Priors , 2011, IEEE Transactions on Neural Networks.

[51]  Samuel Kaski,et al.  Bayesian CCA via Group Sparsity , 2011, ICML.

[52]  Neil D. Lawrence,et al.  A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression , 2011, BMC Bioinformatics.

[53]  Neil D. Lawrence,et al.  Variational Gaussian Process Dynamical Systems , 2011, NIPS.

[54]  Ian D. Reid,et al.  Nonlinear shape manifolds as shape priors in level set segmentation and tracking , 2011, CVPR 2011.

[55]  Ian D. Reid,et al.  Shared shape spaces , 2011, 2011 International Conference on Computer Vision.

[56]  Sergey Levine,et al.  Continuous character control with low-dimensional embeddings , 2012, ACM Trans. Graph..

[57]  Peter N. Belhumeur,et al.  Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification , 2012, BMVC.

[58]  Neil D. Lawrence,et al.  Manifold Relevance Determination , 2012, ICML.

[59]  Xiaoou Tang,et al.  Surpassing Human-Level Face Verification Performance on LFW with GaussianFace , 2014, AAAI.