https://github.com/jonathanwenger/itergp

IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)
https://github.com/jonathanwenger/itergp

gaussian-processes machine-learning neurips-2022 numerical-linear-algebra probabilistic-numerics

Last synced: 3 months ago
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IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)

• Host: GitHub
• URL: https://github.com/jonathanwenger/itergp
• Owner: JonathanWenger
• License: mit
• Created: 2022-02-01T00:30:47.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-04-12T14:00:38.000Z (about 2 years ago)
• Last Synced: 2025-03-18T09:05:17.027Z (3 months ago)
• Topics: gaussian-processes, machine-learning, neurips-2022, numerical-linear-algebra, probabilistic-numerics
• Language: Python
• Homepage: https://itergp.readthedocs.io
• Size: 19.9 MB
• Stars: 40
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# IterGP: Computation-Aware Gaussian Process Inference

This repository contains an implementation of the framework described in the paper [Posterior and Computational Uncertainty in Gaussian Processes](https://arxiv.org/abs/2205.15449).

## Installation

You can install the Python package via `pip`:

```bash

pip install git+https://github.com/JonathanWenger/itergp.git

```

## Documentation and Tutorials

To understand how to use the functionality of IterGP, take a look at the [API reference](https://itergp.readthedocs.io/en/latest/api.html) and the [tutorials](https://itergp.readthedocs.io/en/latest/tutorials.html).

## Datasets

Any datasets used in the experiments can be accessed via the API:

```python

from itergp import datasets

data = datasets.uci.BikeSharing(dir="data/uci")

data.train.y

# array([ 0.20011634, -2.74432264,  0.14604912, ...,  0.40556032,

#         0.57590568, -0.54709806])

```

If the dataset is not already cached, it will be downloaded and cached locally.

## Citation

```bibtex

@inproceedings{wenger2022itergp,

  author    = {Jonathan Wenger and Geoff Pleiss and Marvin Pf{\"o}rtner and Philipp Hennig and John P. Cunningham},

  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},

  keywords  = {gaussian processes, probabilistic numerics, numerical analysis},

  title     = {Posterior and Computational Uncertainty in {G}aussian processes},

  url       = {https://arxiv.org/abs/2205.15449},

  year      = {2022}

}

```