FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery [AISTATS 2023]

Official implementation of our AISTATS 2023 paper "FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery" (29% acceptance rate).

Requirements

Linux machine (experiments were run on Ubuntu 18.04.5 LTS and Ubuntu 20.04.2 LTS)
Anaconda (alternatively, you may install the packages in environment.yml manually)

Setup

Run the following command to install the required Python packages into a new environment named CAL using Anaconda.

conda env create -f environment.yml

Notes on the differential equation experiments:

The npde_master directory provides the necessary functions for handling differential equation-based data generation/computation.
To run the differential equations experiments requires setting up and activating a tensorflow-based environment, which is found in environment-TF.yml.

Running experiments

In the main directory,

Change current environment to the CAL environment.

conda activate CAL

Run the desired experiment. Files with names exp_*.py are scripts for different experiments.

python synthetic_run_1D.py executes the experiments on the 1-dimensional synthetic data
python de-ode_run.py executes the experiments on the differential equation data where the true function is an ordinary differential equation. Note that to run this experiments requires the tensorflow-based environment.

Jupyter notebooks

Several notebooks for experiments with different datasets and analyzing results are provided for interactively developling and testing the methods. Visualization of the data and results is also provided. You can execute the notebooks under the correct conda environment: environment.yml for non-differential equation experiments and environment-TF.yml for differential equation experiments.

License

This code is released under the MIT License.

Citing our paper

If you find our paper relevant or use our code in your research, please consider citing our paper:

@InProceedings{Xu2023,
  title={FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery},
  author={Xinyi Xu and Zhaoxuan Wu and Chuan Sheng Foo and Bryan Kian Hsiang Low},
  booktitle={Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS-23)},
  year={2023}
}

XinyiYS/FAIR