Dynamic Shapley Value Computation

Code for implementation of "Dynamic Shapley Value Computation".

Please cite the following work if you use this benchmark or the provided tools or implementations:

@inproceedings{DBLP:conf/icde/zhang2023dynamic,
  author    = {Jiayao Zhang and
               Haocheng Xia and
               Qiheng Sun and
               Jinfei Liu and
               Li Xiong and
               Jian Pei and
               Kui Ren},
  title     = {Dynamic Shapley Value Computation},
  booktitle = {39th {IEEE} International Conference on Data Engineering, {ICDE} 2023,
               Anaheim, California, USA, April 3–7, 2023},
  publisher = {{IEEE}},
  year      = {2023}
}

Prerequisites

• Python, NumPy, Scikit-learn, Tqdm

Experiments in the Paper

They can be found in folder paper_exps.

Basic Usage

To divide value fairly between individual train data points/sources which are dynamic, given the learning algorithm and a measure of performance for the trained model (test accuracy, etc.).

Run Example Experiments

$ python3 examples.py

If you have browser env, jupyter notebook is recommended.

$ jupyter_notebook examples.ipynb

Documents

More detailed usages and code implementation can refer to the documents.

$ make docs

(* Documents are powered by Sphinx.)

License

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