https://github.com/automl/lcpfn

https://github.com/automl/lcpfn

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/automl/lcpfn
• Owner: automl
• License: mit
• Created: 2023-10-25T07:42:05.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-15T07:13:40.000Z (26 days ago)
• Last Synced: 2024-10-29T20:46:58.312Z (11 days ago)
• Language: Jupyter Notebook
• Size: 192 KB
• Stars: 12
• Watchers: 6
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

        

# Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks

This repository offers an implementation of [LC-PFN](https://openreview.net/pdf?id=xgTV6rmH6n), a method designed for efficient Bayesian learning curve extrapolation.

**LC-PFN in action on [Google colab](https://colab.research.google.com/drive/1JA2t91xgqZVfjZya41oW5vVQktv_YXhE?usp=sharing) and [HuggingFace](https://huggingface.co/spaces/herilalaina/lcpfn)**

Installation using pip:

```bash

pip install -U lcpfn

```

> **Update**: there is an inconsistency between the code and the paper regarding the definition of the noise prior. The correct definition is the one used in the code, where $\log(\sigma)$ is defined as $\mathcal{N}(-4, 1)$.

### Usage

Try out the `notebooks` (require ``matplotlib``) for training and inference examples.

**NOTE:**  Our model supports only increasing curves with values in $[0,1]$. If needed, please consider normalizing your curves to meet these constraints. See an example in ``notebooks/curve_normalization.ipynb``.

### Reference

```

@inproceedings{

adriaensens2023lcpfn,

title={Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks},

author={Adriaensen, Steven and Rakotoarison, Herilalaina and Müller, Samuel and Hutter, Frank},

booktitle={Thirty-seventh Conference on Neural Information Processing Systems},

year={2023},

url={https://openreview.net/forum?id=xgTV6rmH6n}

}

```