This repository contains the official code for our ICML 2024 paper. This main branch provides the Freeze-Thaw PFN surrogate (FT-PFN) surrogate model as a drop-in surrogate for multi-fidelity Bayesian Optimization loops. Along with the synthetic prior generation and training code. To reproduce experiments from the above paper version, please refer to the branch icml-2024.
To use the ifBO algorithm in practice, please refer to NePS, a package for hyperparameter optimization that maintains the latest, improved ifBO version (TBA, TODO).
| Version | Identifier | Notes |
|---|---|---|
| 0.0.1 | ICML '24 submission | FT-PFN from ifBO, trained on LCNet curves, DPL power law, broke scaling law |
TBA
If using our surrogate, code, experiment setup, kindly cite using:
@inproceedings{
rakotoarison-icml24,
title={In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization},
author={H. Rakotoarison and S. Adriaensen and N. Mallik and S. Garibov and E. Bergman and F. Hutter},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=VyoY3Wh9Wd}
}