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Gaussian Process package based on data augmentation, sparsity and natural gradients
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benchmark Updated doc and use of optimizers Jul 1, 2019
examples Updated doc and use of optimizers Jul 1, 2019
test Updated doc and use of optimizers Jul 1, 2019
.codecov.yml OMGP.jl generated files. Feb 15, 2018
.travis.yml Change name from OMGP to AugmentedGaussianProcesses Oct 26, 2018
REQUIRE Updated version number Apr 9, 2019

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AugmentedGaussianProcesses! (previously OMGP) is a Julia package in development for Data Augmented Sparse Gaussian Processes. It contains a collection of models for different gaussian and non-gaussian likelihoods, which are transformed via data augmentation into conditionally conjugate likelihood allowing for extremely fast inference via block coordinate updates.

Packages models :

Two GP classification likelihood

Three GP Regression likelihood

  • Gaussian : The standard Gaussian Process regression model with a Gaussian Likelihood (no data augmentation was needed here) IJulia example/Reference
  • StudentT : The standard Gaussian Process regression with a Student-t likelihood (the degree of freedom ν is not optimizable for the moment) IJulia example/Reference
  • Laplace : Gaussian Process regression with a Laplace likelihood IJulia example/(No reference at the moment)
  • Heteroscedastic : Regression with non-stationary noise, given by an additional GP. (no reference at the moment)

One Multi-Class Classification Likelihood

  • Logistic-SoftMax : A modified version of the softmax where the exponential is replaced by the logistic function IJulia example/Reference

More models in development

  • Poisson : For point process estimation
  • Heteroscedastic : Non stationary noise
  • Probit : A Classifier with a Bernoulli likelihood with the probit link
  • Online : Allowing for all algorithms to work online as well
  • Numerical solving : Allow for a more general class of likelihoods by applying numerical solving (like GPFlow)

Install the package

The package requires Julia 1.1 Run in Julia press ] and type add AugmentedGaussianProcesses, it will install the package and all its requirements

Use the package

A complete documentation is currently being written, for now you can use this very basic example where X_train is a matrix N x D where N is the number of training points and D is the number of dimensions and Y_train is a vector of outputs (or matrix independent multi-output).

using AugmentedGaussianProcesses
model = SVGP(X_train,Y_train,RBFKernel(1.0),LogisticLikelihood(),AnalyticSVI(100),64)
Y_predic = predict_y(model,X_test) #For getting the label directly
Y_predic_prob = proba_y(model,X_test) #For getting the likelihood of predicting class 1

Both documentation and examples are available.

References :

Check out my website for more news

"Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K.I. Williams

UAI 19' "Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation" by Théo Galy-Fajou, Florian Wenzel, Christian Donner and Manfred Opper

ECML 17' "Bayesian Nonlinear Support Vector Machines for Big Data" by Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch and Marius Kloft.

AAAI 19' "Efficient Gaussian Process Classification using Polya-Gamma Variables" by Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft and Manfred Opper.

UAI 13' "Gaussian Process for Big Data" by James Hensman, Nicolo Fusi and Neil D. Lawrence

JMLR 11' "Robust Gaussian process regression with a Student-t likelihood." by Jylänki Pasi, Jarno Vanhatalo, and Aki Vehtari.

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