In-context Bayesian Learning Curve Extrapolation
Project description
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
This repository offers an implementation of LC-PFN, a method designed for efficient Bayesian learning curve extrapolation.
LC-PFN in action on Google colab and HuggingFace
Installation using pip:
pip install -U lcpfn
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}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
lcpfn-0.1.3.tar.gz
(29.2 kB
view details)
Built Distribution
lcpfn-0.1.3-py3-none-any.whl
(32.3 kB
view details)
File details
Details for the file lcpfn-0.1.3.tar.gz
.
File metadata
- Download URL: lcpfn-0.1.3.tar.gz
- Upload date:
- Size: 29.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.16
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4115cff9451229a7d10d7b56d8d7ad1a12a988f2f9066474581c43345a87588a |
|
MD5 | e50f1025e85b828c29ca37c59b0adf04 |
|
BLAKE2b-256 | 0e87d436494b97c38bd394fd7b8fa58651f853b0518aba92ecd823ab699b680c |
File details
Details for the file lcpfn-0.1.3-py3-none-any.whl
.
File metadata
- Download URL: lcpfn-0.1.3-py3-none-any.whl
- Upload date:
- Size: 32.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.16
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7416cb91d189ffcd7ed42e5b5a551e3ee73896518c799e1ff062ed91e538c8a1 |
|
MD5 | 5071bf89f0b535743d02bddcced069d4 |
|
BLAKE2b-256 | 18f328d0a3b6c38d766638e1d0b2c0b1adb93c817646842a652a68174861edc5 |