https://github.com/wangyongjie-ntu/c-chvae-pytorch
The reproduction of c-chvae.
https://github.com/wangyongjie-ntu/c-chvae-pytorch
Last synced: 3 months ago
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The reproduction of c-chvae.
- Host: GitHub
- URL: https://github.com/wangyongjie-ntu/c-chvae-pytorch
- Owner: wangyongjie-ntu
- License: mit
- Created: 2021-03-30T05:55:17.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-30T08:14:57.000Z (about 4 years ago)
- Last Synced: 2025-01-09T19:57:45.083Z (4 months ago)
- Language: Python
- Size: 3.91 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# C-CHVAE-Pytorch
Counterfactual explanation is the meaningful and minimum perturbation for an input that can alter the original prediction by a machine learning model, usually from an undesirable prediction to a desirable one. In this repo, I plan to reproduce the implementation of C-CHVAE [WWW2020](https://github.com/MartinPawel/c-chvae) on PyTorch platform. The official implementation works on Tensorflow. Thanks for Martin providing the test file to me which help me understand the algorithms.
## Getting start
## Usage
## Demo
## Bibtex
```
@inproceedings{pawelczyk_learning2019,
author = {Pawelczyk, Martin and Broelemann, Klaus and Kasneci, Gjergji},
title = {Learning Model-Agnostic Counterfactual Explanations for Tabular Data},
year = {2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of The Web Conference 2020},
pages = {3126–3132},
numpages = {7},
keywords = {Transparency, Counterfactual explanations, Interpretability},
location = {Taipei, Taiwan},
series = {WWW '20}
}
```