This repository contains the code for the experiments presented in the paper End-to-End Learning for Stochastic Optimization: A Bayesian Perspective, accepted as a poster at ICML 2023. The paper proposes a general framework to model end-to-end learning approaches for stochastic optimization problems and analyze their behavior. It provides a rigorous Bayesian interpretation for a popular existing end-to-end learning approach, and formulates end-to-end learning algorithmic counterparts to empirical risk minimization and distributionally robust optimization - two standard stochastic optimization methodologies. The experiments illustrate the theoretical results using a synthetic newsvendor problem and an economic energy dispatch problem.
The code is written in Python. All required packages can be found in the requirements.txt file, we use Python 3.9.12 for our experiments. To install the dependencies, run the following command:
pip install -r requirements.txt
Instructions on how to run the code for each experiment can be found in the corresponding subfolders.
Energy Dispatch: the code for the energy dispatch experimentGradient Projection: the code for the gradient projection experimentMean Estimation: the code for the mean estimation experimentNewsvendor experiment: the code for the newsvendor experiment
If you use our code or findings in your research, please cite our paper as follows:
@inproceedings{rychener2023end2end,
title={End-to-end learning for stochastic optimization: A bayesian perspective},
author={Rychener, Yves and Kuhn, Daniel and Sutter, Tobias},
booktitle={International Conference on Machine Learning},
pages={29455--29472},
year={2023},
organization={PMLR}
}
For questions or support, please contact Yves Rychener.
This project is licensed under the MIT License.