This is the code used to generate the results for the ICML 2021 conference paper "Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss".
This code has the following requirements:
- MATLAB 2019b or later
- MOSEK solver (https://www.mosek.com/)
- The MATLAB Statistics and Machine Learning Toolbox (https://uk.mathworks.com/products/statistics.html)
- The MATLAB Parallel Computing Toolbox (https://uk.mathworks.com/products/parallel-computing.html)
- The MATLAB Optimization Toolbox (https://uk.mathworks.com/products/optimization.html)
All experimental results in our paper are saved as csv format in the file of "result". The file "datasets" is where our synthetic and real-world datasets are placed. To save space, we only put wine and insurance datasets in "datasets" file, you can download other real-world datasets from the following websites.
- BlogFeedBack Dataset (https://archive.ics.uci.edu/ml/datasets/BlogFeedback)
- Residential Building Dataset (https://archive.ics.uci.edu/ml/datasets/Residential+Building+Data+Set)
If you want to generate synthetic data, please run the ipynb file make_regression_dataset.ipynb. Then the correspondingly synthetic datasets would be placed at file "datasets/synthetic/".
- To run the experiments of real datasets, please
run main_real_dataset_mosek.m. - To run the experiments of synthetic datasets, please
run main_synthetic_mosek.m.
- To plot the average mean squared error (MSE) of different methods, you can use the function
plot_mse(csvname), where "csvname" is the path of corresponding MSE csv file. For example,csvname = './result/wine_modest_None_mse.csv' - To obtain the figures of time comparison, you can use the function
plot_time(csvname), where "csvname" is the path of corresponding time csv file. For example,csvname = './result/wine_modest_None_time.csv'
If you found the provided code useful, please cite our work.
@InProceedings{pmlr-v139-wang21d,
title = {Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss},
author = {Wang, Jiali and Chen, He and Jiang, Rujun and Li, Xudong and Li, Zihao},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {10708--10716},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/wang21d/wang21d.pdf},
url = {https://proceedings.mlr.press/v139/wang21d.html}
}