## This package is being deprecated in favor of the JuliaGaussianProcesses ecosystem. In particular most of the features of augmentations is being moved to a new package called AugmentedGPLikelihoods.jl. It is still a work in progress though.

AugmentedGaussianProcesses.jl 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. There are also more options to use more traditional **variational inference** via quadrature or Monte Carlo integration.

The theory for the augmentation is given in the following paper : Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models

### You can also use the package in Python via PyJulia!

# Packages models :

## Two GP classification likelihoods

**BayesianSVM**: A Classifier with a likelihood equivalent to the classic SVM IJulia example/Reference**Logistic**: A Classifier with a Bernoulli likelihood with the logistic link IJulia example/Reference

## Four GP Regression likelihoods

**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)

## Two GP event counting likelihoods

**Discrete Poisson Process**: Estimating a the Poisson parameter λ at every point (as λ₀σ(f)). (no reference at the moment)**Negative Binomial**: Estimating the success probability at every point for a negative binomial distribution (no reference at the miment)

## 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

## Multi-Ouput models

- It is also possible to create a multi-ouput model where the outputs are a linear combination of inducing variables see IJulia example in preparation/[Reference][neuripsmultiouput]

## More models in development

**Probit**: A Classifier with a Bernoulli likelihood with the probit link**Online**: Allowing for all algorithms to work online as well

## Install the package

The package requires at least Julia 1.3
Run `julia`

, press `]`

and type `add AugmentedGaussianProcesses`

, it will install the package and all its dependencies.

## Use the package

A complete documentation is available in the docs. For a short start 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 of independent outputs).

using AugmentedGaussianProcesses;
using KernelFunctions
model = SVGP(SqExponentialKernel(), LogisticLikelihood(), AnalyticSVI(100), 64)
train!(model, X_train, Y_train, 100)
Y_predic = predict_y(model, X_test) # For getting the label directly
Y_predic_prob, Y_predic_prob_var = proba_y(model, X_test) # For getting the likelihood (and likelihood uncertainty) of predicting class 1

Both documentation and examples/tutorials are available.

## References :

Check out my website for more news

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

AISTATS 20' "Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models" by Théo Galy-Fajou, Florian Wenzel and Manfred Opper [https://arxiv.org/abs/2002.11451][autoconj]

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

NeurIPS 18' "Moreno-Muñoz, Pablo, Antonio Artés, and Mauricio Álvarez. "Heterogeneous multi-output Gaussian process prediction." Advances in Neural Information Processing Systems. 2018." [https://papers.nips.cc/paper/7905-heterogeneous-multi-output-gaussian-process-prediction][neuripsmultiouput]

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