Bilevel Optimization Made Easy (BOME)

This repo contains the source code for BOME, which has been accepted to NeurIPS 2022.

This repo is still under construction, please feel free to reach out via [email protected] if you have any question.

Data cleaning and L2 Regularization

Please go the hpo folder and follow the grid.py as an example to run the code as well as other baseline methods. For your convenience, we have pre-processed the dataset and saved them into save_data_cleaning and save_l2reg folders respectively.

Please download the dataset and unzip it under the hpo folder.

Toys (adversarial, low-level singleton, coreset selection)

Please go to the toy folder and read the corresponding python script.

Citations

If you find our work interesting or the repo useful, please consider citing this paper:

@article{ye2022bome,
  title={Bome! bilevel optimization made easy: A simple first-order approach},
  author={Liu, Bo and Ye, Mao and Wright, Stephen and Stone, Peter and Liu, Qiang},
  journal={arXiv preprint arXiv:2209.08709},
  year={2022}
}