[ANN] AugmentedGaussianProcesses.jl

#1

Hi,
Just want to make so shameless advertisement for my Gaussian Process package : AugmentedGaussianProcesses.jl
With it you can work efficiently with non-gaussian likelihood (as well as gaussian of course) while scaling with high number of data.
It features :

  • Non-Gaussian likelihoods : Student-T, Bernoulli (logistic link), Bayesian-SVM (hinge loss) and a multi-class likelihood (similar to softmax), and more incoming
  • Scalability via inducing points (scalable to 1e6 points via stochastic updates)
  • Inference via : Numerical Variational Inference (gradients approximated numerically), Analytic Variational Inference (via an augmentation trick!), Gibbs Sampling
  • Hyperparameter optimization included (but to be improved)

Feature incoming :

  • Online learning
  • More kernel functions
  • Integration of AD

Please check it out, any feedback is welcome :slight_smile:
PS: Also I am alone on this project so if anyone is interested to work on this as well, this would be amazing

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