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### machine learning.

DATA - GAUSSIAN PROCESS Data This page contains links to some of the data sets used in the book for demonstration purposes. USPS handwritten digit data The usps handwritten image data are contained in the file usps_resampled.mat available as bz2 (7.0 Mb, unpack using "tar -xjf usps_resampled.tar.bz2") or zip (8.3 Mb) archives. Besides the data file, the archive also contains a tiny script loadBinaryUSPS.m for GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. 2006 Massachusetts Institute of Technology.c www GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, k ) MODEL SELECTION AND ADAPTATION OF HYPERPARAMETERS Model Selection and Adaptation of Hyperparameters 5 GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, >)>)>) > > I) > >, > > > > > > > >) >### >.

DOCUMENTATION FOR GPML MATLAB CODE Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning.It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. RELATIONSHIPS BETWEEN GPS AND OTHER MODELS### e 2) =. = =) = =))>

APPROXIMATION METHODS FOR LARGE DATASETS C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ∆ 8:) = i) >) = > > >) = THE GAUSSIAN PROCESSES WEB SITE The Gaussian Processes Web Site GAUSSIAN PROCESSES FOR MACHINE LEARNING: BOOK WEBPAGEDATACONTENTSORDERERRATAAUTHORSSOFTWARE Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in### machine learning.

DATA - GAUSSIAN PROCESS Data This page contains links to some of the data sets used in the book for demonstration purposes. USPS handwritten digit data The usps handwritten image data are contained in the file usps_resampled.mat available as bz2 (7.0 Mb, unpack using "tar -xjf usps_resampled.tar.bz2") or zip (8.3 Mb) archives. Besides the data file, the archive also contains a tiny script loadBinaryUSPS.m for GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. 2006 Massachusetts Institute of Technology.c www GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, k ) MODEL SELECTION AND ADAPTATION OF HYPERPARAMETERS Model Selection and Adaptation of Hyperparameters 5 GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, >)>)>) > > I) > >, > > > > > > > >) >### >.

DOCUMENTATION FOR GPML MATLAB CODE Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning.It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. RELATIONSHIPS BETWEEN GPS AND OTHER MODELS### e 2) =. = =) = =))>

APPROXIMATION METHODS FOR LARGE DATASETS C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ∆ 8:) = i) >) = > > >) = THE GAUSSIAN PROCESSES WEB SITE The Gaussian Processes Web Site THE GAUSSIAN PROCESSES WEB SITE Books. Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Chris Williams, the MIT Press, 2006, online version.. Statistical Interpolation of Spatial Data: Some Theory for Kriging, Michael L. Stein, Springer, 1999.. Statistics for Spatial Data (revised edition), Noel A. C. Cressie, Wiley, 1993. Spline Models for Observational Data, Grace Wahba, SIAM, 1990 GAUSSIAN PROCESSES FOR MACHINE LEARNING: CONTENTS Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. DATA - GAUSSIAN PROCESS Data This page contains links to some of the data sets used in the book for demonstration purposes. USPS handwritten digit data The usps handwritten image data are contained in the file usps_resampled.mat available as bz2 (7.0 Mb, unpack using "tar -xjf usps_resampled.tar.bz2") or zip (8.3 Mb) archives. Besides the data file, the archive also contains a tiny script loadBinaryUSPS.m for DOCUMENTATION FOR GPML MATLAB CODE Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning.It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. GAUSSIAN PROCESSES FOR MACHINE LEARNING### c ) = i i /

GAUSSIAN PROCESSES FOR MACHINE LEARNING Title: Gaussian Processes for Machine Learning Author: Carl Edward Rasmusen and Christopher K. I. Williams Created Date: 5/23/2007### 10:27:05 AM

HOW TO ORDER THE BOOK How to order the Book. The book is 8" × 10", 272 p. hardcover and has a list price of 35.00 US$ or 22.95 UK£. In the table below are some book stores carrying the book: FURTHER ISSUES AND CONCLUSIONS C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, K = | > ,) THE GAUSSIAN PROCESSES WEB SITE The Gaussian Processes Web Site ERRATA - GAUSSIAN PROCESS Errata for the second printing p. 50, algorithm 3.3, line 15 and 16, the term "+\sum i \log()" has the wrong sign, the correct term is "-\sum_ i \log()" in both line 15 and 16. (thanks to Chris Mansley) GAUSSIAN PROCESSES FOR MACHINE LEARNING: BOOK WEBPAGEDATACONTENTSORDERERRATAAUTHORSSOFTWARE Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in### machine learning.

GAUSSIAN PROCESSES FOR MACHINE LEARNING: CONTENTS Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. 2006 Massachusetts Institute of Technology.c www DATA - GAUSSIAN PROCESSMULTIVARIATE GAUSSIAN PROCESSGAUSSIAN PROCESS BOOKGAUSSIAN PROCESS PDFGAUSSIAN PROCESS PYTHONGAUSSIAN PROCESS### REGRESSION

Data This page contains links to some of the data sets used in the book for demonstration purposes. USPS handwritten digit data The usps handwritten image data are contained in the file usps_resampled.mat available as bz2 (7.0 Mb, unpack using "tar -xjf usps_resampled.tar.bz2") or zip (8.3 Mb) archives. Besides the data file, the archive also contains a tiny script loadBinaryUSPS.m for GAUSSIAN PROCESSES FOR MACHINE LEARNINGGAUSSIAN PROCESSES FOR MACHINE### LEARNING

Gaussian Processes for Machine Learning 3 > GAUSSIAN PROCESSES FOR MACHINE LEARNINGGAUSSIAN PROCESSES FOR MACHINE LEARNING PDFGAUSSIAN PROCESSES IN MACHINE LEARNINGGAUSSIAN PROCESS FOR MACHINE LEARNINGGAUSSIAN PROCESS FOR MACHINE LEARNING BIBT…ADAPTIVE COMPUTATION AND MACHINE LEARNINGGAUSSIAN PROCESS REGRESSION MACHINE### LEAR…

C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, k ) DOCUMENTATION FOR GPML MATLAB CODE Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning.It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. MODEL SELECTION AND ADAPTATION OF HYPERPARAMETERSGAUSSIAN PROCESS REGRESSIONMULTIVARIATE GAUSSIAN PROCESS REGRESSIONAN INTUITIVE GUIDE TO GAUSSIAN PROCESSESDEEP GAUSSIAN PROCESSESINTRODUCTION TO GAUSSIAN PROCESSESDOUBLE GAUSSIAN MODEL Model Selection and Adaptation of Hyperparameters 5 RELATIONSHIPS BETWEEN GPS AND OTHER MODELS### e 2) =. = =) = =))>

THE GAUSSIAN PROCESSES WEB SITE The Gaussian Processes Web Site GAUSSIAN PROCESSES FOR MACHINE LEARNING: BOOK WEBPAGEDATACONTENTSORDERERRATAAUTHORSSOFTWARE Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in### machine learning.

GAUSSIAN PROCESSES FOR MACHINE LEARNING: CONTENTS Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. GAUSSIAN PROCESSES FOR MACHINE LEARNING C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. 2006 Massachusetts Institute of Technology.c www DATA - GAUSSIAN PROCESSMULTIVARIATE GAUSSIAN PROCESSGAUSSIAN PROCESS BOOKGAUSSIAN PROCESS PDFGAUSSIAN PROCESS PYTHONGAUSSIAN PROCESS### REGRESSION

Data This page contains links to some of the data sets used in the book for demonstration purposes. USPS handwritten digit data The usps handwritten image data are contained in the file usps_resampled.mat available as bz2 (7.0 Mb, unpack using "tar -xjf usps_resampled.tar.bz2") or zip (8.3 Mb) archives. Besides the data file, the archive also contains a tiny script loadBinaryUSPS.m for GAUSSIAN PROCESSES FOR MACHINE LEARNINGGAUSSIAN PROCESSES FOR MACHINE### LEARNING

Gaussian Processes for Machine Learning 3 > GAUSSIAN PROCESSES FOR MACHINE LEARNINGGAUSSIAN PROCESSES FOR MACHINE LEARNING PDFGAUSSIAN PROCESSES IN MACHINE LEARNINGGAUSSIAN PROCESS FOR MACHINE LEARNINGGAUSSIAN PROCESS FOR MACHINE LEARNING BIBT…ADAPTIVE COMPUTATION AND MACHINE LEARNINGGAUSSIAN PROCESS REGRESSION MACHINE### LEAR…

C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, k ) DOCUMENTATION FOR GPML MATLAB CODE Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning.It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. MODEL SELECTION AND ADAPTATION OF HYPERPARAMETERSGAUSSIAN PROCESS REGRESSIONMULTIVARIATE GAUSSIAN PROCESS REGRESSIONAN INTUITIVE GUIDE TO GAUSSIAN PROCESSESDEEP GAUSSIAN PROCESSESINTRODUCTION TO GAUSSIAN PROCESSESDOUBLE GAUSSIAN MODEL Model Selection and Adaptation of Hyperparameters 5 RELATIONSHIPS BETWEEN GPS AND OTHER MODELS### e 2) =. = =) = =))>

THE GAUSSIAN PROCESSES WEB SITE The Gaussian Processes Web Site THE GAUSSIAN PROCESSES WEB SITE Books. Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Chris Williams, the MIT Press, 2006, online version.. Statistical Interpolation of Spatial Data: Some Theory for Kriging, Michael L. Stein, Springer, 1999.. Statistics for Spatial Data (revised edition), Noel A. C. Cressie, Wiley, 1993. Spline Models for Observational Data, Grace Wahba, SIAM, 1990 GAUSSIAN PROCESSES FOR MACHINE LEARNING: CONTENTS Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. GAUSSIAN PROCESSES FOR MACHINE LEARNING### c ) = i i /

GAUSSIAN MARKOV PROCESSES Gaussian Markov Processes for i = GAUSSIAN PROCESSES FOR MACHINE LEARNING Title: Gaussian Processes for Machine Learning Author: Carl Edward Rasmusen and Christopher K. I. Williams Created Date: 5/23/2007### 10:27:05 AM

HOW TO ORDER THE BOOK How to order the Book. The book is 8" × 10", 272 p. hardcover and has a list price of 35.00 US$ or 22.95 UK£. In the table below are some book stores carrying the book: THE GAUSSIAN PROCESSES WEB SITE The Gaussian Processes Web Site THE GPML TOOLBOX VERSION 4 The GPML Toolbox version 4.2 Carl Edward Rasmussen & Hannes Nickisch August 22, 2018 Abstract The GPML toolbox is an Octave 3.2.x and Matlab 7.x implementation of inference and pre- FURTHER ISSUES AND CONCLUSIONS C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, K = | > ,) ERRATA - GAUSSIAN PROCESS Errata for the second printing p. 50, algorithm 3.3, line 15 and 16, the term "+\sum i \log()" has the wrong sign, the correct term is "-\sum_ i \log()" in both line 15 and 16. (thanks to Chris Mansley) THE GAUSSIAN PROCESSES WEB SITE This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian processes. Although Gaussian processes have a long history in the field of statistics, they seem to have been employed extensively only in niche areas. With the advent of kernel machines in the machine learning community, models based on Gaussian processes have become commonplace for problems of regression (kriging) and classification as well as a host of more specialized applications.### BOOKS

Gaussian Processes for Machine Learning , Carl Edward Rasmussen and Chris Williams, the MIT Press, 2006, online version### .

Statistical Interpolation of Spatial Data: Some Theory for Kriging### ,

Michael L. Stein, Springer, 1999. Statistics for Spatial Data (revised edition), Noel A. C. Cressie, Wiley, 1993 Spline Models for Observational Data , Grace Wahba, SIAM, 1990 FUTURE AND PAST EVENTS The Bayesian Research Kitchen at The Wordsworth Hotel, Grasmere, Ambleside, Lake District, United Kingdom 05 - 07 September 2008.### A tutorial

entitled Advances in Gaussian Processes### on Dec.

### 4th at NIPS 2006

in VanCouver, slides### , lecture

### .

The Gaussian Processes in Practice workshop at Bletchley Park, U.K., June 12-13 2006. The Open Problems in Gaussian Processes for Machine Learning workshop at nips*05 in Whistler, December### 10th, 2005.

The Gaussian Process Round Table meeting in Sheffield, June 9-10, 2005. OTHER WEB SITES OF RELATED INTEREST The kernel-machines web site.### Wikipedia entry on

### Gaussian processes.

The ai-geostats web site for spatial statistics and geostatistics. The Bibliography of Gaussian Process Models in Dynamic Systems Modelling web site maintained by### Juš Kocijan .

### SOFTWARE

Andreas Geiger has written a simple Gaussian process regression Java applet### , illustrating the

behaviour of covariance functions and hyperparameters.### PACKAGE

### TITLE

### AUTHOR

### IMPLEMENTATION

### DESCRIPTION

### bcm

The Bayesian Committee Machine### Anton Schwaighofer

### matlab and NETLAB

An extension of the Netlab implementation for GP regression. It allows large scale regression based on the BCM approximation, see also the### accompanying paper

### fbm

Software for Flexible Bayesian Modeling### Radford M. Neal

### C for linux/unix

An extensive and well documented package implementing Markov chain Monte Carlo methods for Bayesian inference in neural networks, Gaussian processes (regression, binary and multi-class classification), mixture models and Dirichlet Diffusion trees.### gp-lvm and fgp-lvm

A (fast) implementation of Gaussian Process Latent Variable Models### Neil D. Lawrence

### matlab and C

### gpml

Code from the Rasmussen and Williams: Gaussian Processes for Machine Learning book. Carl Edward Rasmussen and### Hannes Nickisch

### matlab and octave

The GPML toolbox implements approximate inference algorithms for Gaussian processes such as Expectation Propagation, the Laplace Approximation and Variational Bayes for a wide class of likelihood functions for both regression and classification. It comes with a big algebra of covariance and mean functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x. JMLR paper describing the toolbox.### c++-ivm

Sparse approximations based on the Informative Vector Machine### Neil D. Lawrence

### C++

IVM Software in C++ , also includes the null category noise model for semi-supervised learning.### BFD

Bayesian Fisher's Discriminant software Tonatiuh Peña Centeno### matlab

Implements a Gaussian process interpretation of Kernel Fisher's### discriminant.

### gpor

Gaussian Processes for Ordinal Regression### Wei Chu

### C for linux/unix

Software implementation of Gaussian Processes for Ordinal Regression. Provides Laplace Approximation, Expectation Propagation and Variational Lower Bound.### MCMCstuff

MCMC Methods for MLP and GP and Stuff### Aki Vehtari

### matlab and C

A collection of matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. The purpose of this toolbox was to port some of the features in fbm### to matlab for

easier development for matlab users.### ogp

Sparse Online Gaussian Processes### Lehel Csató

### matlab and NETLAB

Approximate online learning in sparse Gaussian process models for regression (including several non-Gaussian likelihood functions) and### classification.

### sogp

Sparse Online Gaussian Process C++ Library### Dan Grollman

### C++

Sparse online Gaussian process C++ library based on the PhD thesis of### Lehel Csató

### spgp .tgz or

### .zip

Sparse Pseudo-input Gaussian Processes### Ed Snelson

### matlab

Implements sparse GP regression as described in Sparse Gaussian Processes using Pseudo-inputs and Flexible and efficient Gaussian process models for machine learning. The SPGP uses gradient-based marginal likelihood optimization to find suitable basis points and kernel hyperparameters in a single joint optimization.### tgp

Treed Gaussian Processes### Robert B. Gramacy

### C/C++ for R

Bayesian Nonparametric and nonstationary regression by treed Gaussian processes with jumps to the limiting linear model (LLM). Special cases also implememted include Bayesian linear models, linear CART, stationary separable and isotropic Gaussian process regression. Includes 1-d and 2-d plotting functions (with higher dimension projection and slice capabilities), and tree drawing, designed for visualization of tgp class output. See also Gramacy 2007### Tpros

Gaussian Process Regression David MacKay and Mark### Gibbs

### C

Tpros is the Gaussian Process program written by Mark Gibbs and David### MacKay.

### GP Demo

Octave demonstration of Gaussian process interpolation### David MacKay

### octave

This DEMO works fine with octave-2.0 and did not work with 2.1.33.### GPClass

Matlab code for Gaussian Process Classification David Barber and C. K. I. Williams### matlab

Implements Laplace's approximation as described in Bayesian Classification with Gaussian Processes for binary and multiclass### classification.

### VBGP

Variational Bayesian Multinomial Probit Regression with Gaussian### Process Priors

Mark Girolami and Simon### Rogers

### matlab

Implements a variational approximation for Gaussian Process based multiclass classification as described in the paper Variational Bayesian Multinomial Probit Regression.### pyGPs

Gaussian Processes for Regression and Classification### Marion Neumann

### Python

pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification. github### gaussian-process

Gaussian process regression### Anand Patil

### Python

### under development

### gptk

Gaussian Process Tool-Kit### Alfredo Kalaitzis

### R

The gptk package implements a general-purpose toolkit for Gaussian process regression with an RBF covariance function. Based on a MATLAB implementation written by Neil D. Lawrence. Other software that way be useful for implementing Gaussian process### models:

* The NETLAB package### by Ian Nabney

### includes

code for Gaussian process regression and many other useful thing, e.g.### optimisers.

* See Tom Minka 's page on### accelerating matlab

### and his

### lightspeed

### toolbox.

### * Matthias Seeger

### shares his code

### for

Kernel Multiple Logistic Regression, Incomplete Cholesky Factorization and Low-rank Updates of Cholesky Factorizations. * See the software section of www.kernel-machines.org### .

ANNOTATED BIBLIOGRAPHY Below is a collection of papers relevant to learning in Gaussian process models. The papers are ordered according to topic, with occational papers occuring under multiple headings.### TUTORIALS

Several papers provide tutorial material suitable for a first introduction to learning in Gaussian process models. These range from very short over intermediate , to the more elaborate . All of these require only a minimum of prerequisites in the form of elementary probability theory and linear algebra.### REGRESSION

The simplest uses of Gaussian process models are for (the conjugate case of) regression with Gaussian noise. See the approximation section for papers which deal specifically with sparse or fast approximation techniques. O'Hagan 1978 represents an early reference from the statistics comunity for the use of a Gaussian process as a prior over functions, an idea which was only introduced to the machine learning community by Williams and Rasmussen 1996.### CLASSIFICATION

Exact inference in Gaussian process models for classification is not tractable. Several approximation schemes have been suggested, including Laplace's method, variational approximations, mean field methods, Markov chain Monte Carlo and Expectation Propagation. See also the approximation section. Multi-class classification may be treated explicitly, or decomposed into multiple, binary (one against the rest) problems. For introductions, see for example Williams and Barber 1998 or Kuss and Rasmussen 2005. Bounds from the PAC-Bayesian perspective are applied in Seeger 2002. COVARIANCE FUNCTIONS AND PROPERTIES OF GAUSSIAN PROCESSES The properties of Gaussian processes are controlled by the (mean function and) covariance function. Some references here describe difference covariance functions, while others give mathematical characterizations, see eg. Abrahamsen 1997 for a review. Some references describe non-standard covariance functions leading to non-stationarity etc.### MODEL SELECTION

### APPROXIMATIONS

There are two main reasons for doing approximations in Gaussian process models. Either because of analytical intractability such as arrises in classification and regression with non-Gaussian noise. Or in order to gain a computational advantage when using large datasets, by the use of _sparse_ approximations. Some methods address both issues simultaneously. The approximation methods and approximate inference algorithms are quite diverse, see Quiñonero-Candela and Ramussen 2005 for a unifying framework for sparse approximations in the Gaussian regression model. REFERENCES FROM THE STATISTICS COMMUNITY Gaussian processes have a long history in the statistics community. They have been particularly well developed in geostatistics under the name of _kriging_. The papers have been grouped because they are written using a common terminology, and have slightly different focus from typical machine learning papers, CONSISTENCY, LEARNING CURVES AND BOUNDS The papers in this section give theoretical results on _learning curves_, which describe the expected generalization performance as a function of the number of training cases. Consistency addresses the question whether the solution approaches the true data generating process in the limit of infinitely many training examples. REPRODUCING KERNEL HILBERT SPACES REINFORCEMENT LEARNING GAUSSIAN PROCESS LATENT VARIABLE MODELS (GP-LVM)### APPLICATIONS

### OTHER TOPICS

This section contains a very diverse collection of other uses of inference in Gaussian processes, which don't fit well in any of the### above categories.

-------------------------# Details

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