On Optimal Private Online Stochastic Optimization and High Dimensional Decision Making

This repository is the official implementation of On Optimal Private Online Stochastic Optimization and High Dimensional Decision Making by Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao and Jiheng Zhang.

Requirements:

Requires python 3, numpy, matplotlib, etc. Please use the following command to install the dependencies:

pip install -r requirements.txt

Citation:

If you wish to use our repository in your work, please cite our paper:

BibTex:

@inproceedings{han2022dpsteaming,
  title={Optimal Private Streaming SCO in $\ell_p$-geometry with Applications in High Dimensional Online Decision Making},
  author={Han, Yuxuan and Liang, Zhicong and Liang, Zhipeng and Wang, Yang and Yao, Yuan and Zhang, Jiheng},
  booktitle={International Conference on Machine Learning},
  year={2022},
  organization={PMLR}
}

Any question about the scripts can be directed to the authors via email.

License

This project is licensed under the MIT License - see the LICENSE file for details

Generating the figures in the paper:

For generating the figures in the paper please execute the following codes:

1. run the instances to generate the required experiment results data

nohup bash execute.sh &

2. run summarize.py to collect all the statistic from the experiments meta-data

python3 summarize.py

3. run plot-curves-paper-p1.5.ipynb/plot-curves-paper-pinf.ipynb/plot-curves-bandit.ipynb to generate the figures and tables for the "p=1.5"/"p=inf"/"bandit" part.

Generating the table in the paper:

run summarize_table.ipynb notebook to generate the Table 1 and 2 in the paper.