Git Product home page Git Product logo

kshitij-kayastha / rocoursenet Goto Github PK

View Code? Open in Web Editor NEW

This project forked from birkhoffg/rocoursenet

0.0 0.0 0.0 85.62 MB

This is the official repository of the paper "RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model".

Home Page: https://arxiv.org/abs/2206.00700

License: Apache License 2.0

Shell 0.02% Python 5.58% CSS 0.04% Jupyter Notebook 94.36%

rocoursenet's Introduction

RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model

Arxiv DOI:10.1145/3583780.3615040

This repo contains code to reproduce our paper published at CIKM 2023.

To cite this paper:

@inproceedings{guo2023rocoursenet,
author = {Guo, Hangzhi and Jia, Feiran and Chen, Jinghui and Squicciarini, Anna and Yadav, Amulya},
title = {RoCourseNet: Robust Training of a Prediction Aware Recourse Model},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615040},
doi = {10.1145/3583780.3615040},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {619โ€“628},
numpages = {10},
keywords = {explainable artificial intelligence, adversarial machine learning, counterfactual explanation, algorithmic recourse, interpretability},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}

Install

This project uses jax-relax (a fast and scalable recourse explanation library). Ths library is highly scalable and extensible, which enables our experiments to be finished within 30 minutes. In contrast, a pytorch implementation of RoCourseNet takes around 12 hours to run.

pip install -e ".[dev]" --upgrade

Run Experiments

Running scripts.experiment.py with different arguments will reproduce results in our paper. For example,

  1. Train and Evaluate RoCourseNet on Loan Application Dataset:
python -m scripts.experiment.py -d loan
  1. Train and Evaluate CounterNet on Loan Application Dataset:
python -m scripts.experiment.py -m CounterNet -d loan
  1. Train and Evaluate ROAR on Loan Application Dataset:
python -m scripts.experiment.py -m ROAR -d loan

rocoursenet's People

Contributors

birkhoffg avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.