Shallow and Deep Nonparametric Convolutions for Gaussian Processes

This repository contains PyTorch implementations of the nonparametric convolved Gaussian process (NP-CGP) and nonparametric deep Gaussian process (NP-DGP) outlined in the paper, Shallow and Deep Nonparametric Convolutions for Gaussian Processes.

Setup & Requirements

To install, create a fresh Python 3.9 conda environment and run pip install -e . from root directory. Running setup.py this way will work fine with most GPUs (inc. NVIDIA V100s), but to use NVIDIA A100s, you must also load cuda==11.1.1 and run the following:

pip install torch==1.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Training

Use the following command to run one of the shallow UCI experiments from the paper:

python bin/experiments/uci.py --time 1000 --verbosity 100 --n_iter 40000 --uci_name energy --output_dir jobs/energy --batch_size 1000 --num_layers 1 --dry_run

The dry_run argument allows the model to be trained without Weights & Biases monitoring.

Citation

@article{mcdonald2022shallow,
  title={Shallow and Deep Nonparametric Convolutions for Gaussian Processes},
  author={McDonald, Thomas M and Ross, Magnus and Smith, Michael T and {\'A}lvarez, Mauricio A},
  journal={arXiv preprint arXiv:2206.08972},
  year={2022}
}