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Gaussian Process package based on data augmentation, sparsity and natural gradients
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README.md

AugmentedGaussianProcesses.jl Docs Latest Docs Stable Build Status Coverage Status

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)
train!(model,iterations=100)
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 https://arxiv.org/abs/1905.09670

ECML 17' "Bayesian Nonlinear Support Vector Machines for Big Data" by Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch and Marius Kloft. https://arxiv.org/abs/1707.05532

AAAI 19' "Efficient Gaussian Process Classification using Polya-Gamma Variables" by Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft and Manfred Opper. https://arxiv.org/abs/1802.06383

UAI 13' "Gaussian Process for Big Data" by James Hensman, Nicolo Fusi and Neil D. Lawrence https://arxiv.org/abs/1309.6835

JMLR 11' "Robust Gaussian process regression with a Student-t likelihood." by Jylänki Pasi, Jarno Vanhatalo, and Aki Vehtari. http://www.jmlr.org/papers/v12/jylanki11a.html

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