Spatio-Temporal Variational GPs

This repository is the official implementation of the methods in the publication:

• O. Hamelijnck, W.J. Wilkinson, N.A. Loppi, A. Solin, and T. Damoulas (2021). Spatio-temporal variational Gaussian processes. In Neural Information Processing Systems (NeurIPS). [arXiv]

Citing this work:

@inproceedings{hamelijnck2021spatio,
	title={Spatio-Temporal Variational {G}aussian Processes},
	author={Hamelijnck, Oliver and Wilkinson, William and Loppi, Niki and Solin, Arno and Damoulas, Theodoros},
	booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
	year={2021},
}

Experiment Setup

This has been tested on a Macbook Pro. All spatio-temporal VGP models have been implemented within the Bayes-Newton package.

Environment Setup

We recommend using conda:

conda create -n spatio_gp python=3.7
conda activate spatio_gp

Then install the required python packages:

pip install -r requirements.txt

Data Download

Pre-processed Data

All data, preprocessed and split into train-test splits used in the paper is provided at https://doi.org/10.5281/zenodo.4531304. Download the folder and place the corresponding datasets into experiments/*/data folders.

Manual Data Setup

We also provide scripts to generate the data manually:

make data

which will download the relevant London air quality and NYC data, clean them, and split into train-test splits.

Running Experiments

To run all experiments across all training folds run:

make experiments

To run an individual experiment refer to the Makefile.

Baselines used

• GPFlow2 : https://github.com/GPflow/GPflow
• GPYTorch: https://github.com/cornellius-gp/gpytorch

License

This software is provided under the MIT license.