Code for NeurIPS 2021 paper 'Spatio-Temporal Variational Gaussian Processes'

Overview

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

License

This software is provided under the MIT license.

Owner
AaltoML
Machine learning group at Aalto University lead by Prof. Solin
AaltoML
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