AIGP: Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes

This repository contains implementation for AISTATS 2019 paper "Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes."

Run the model

We have wrapped our code for a one command run: "python aigp.py"

In aigp.py, you need to specify hyperparameters like the number of parents, SE kernel parameters, likelihood type, and neural network structures. Please refer 'aigp.py' for more details.

Environment requirements

numpy 1.15.1

tensorflow 1.12.0

scipy 1.1.0

scikit-learn 0.19.1

matplotlib 2.2.3