SS-CMPE

Welcome to the README file of the official implementation SS-CMPE project! This repository contains the code implementation for the paper titled "Learning to Solve the Constrained Most Probable Explanation Task".

paper

Setup and Installation

To get started with the project, follow these steps:

1. Create a new conda environment and install the required dependencies by running the following command:

   conda create --name sscmpe --file requirements.txt --channel pytorch --channel nvidia --channel conda-forge

2. Activate the conda environment:

   conda activate sscmpe

Experiments

This repository provides README files for specific sets of experiments in the following folders:

• Tractable Probabilistic Circuits and High Tree-Width Markov Networks (ssl_adv): This folder contains the README file for experiments related to Tractable Probabilistic Circuits and High Tree-Width Markov Networks. Please navigate to the ssl_adv folder to access the specific README instructions.

• Adversarial Example Generation (ssl_pgm): This folder contains the README file for experiments related to Adversarial Example Generation. Please navigate to the ssl_pgm folder to access the specific README instructions.

Please refer to the respective README files in the mentioned folders for detailed instructions on running the experiments and utilizing the provided code.

Citation

If this work is helpful in your research, please consider starring ⭐ us and citing:

@inproceedings{arya_2024_solveconstraineda,
  title = {Learning to {{Solve}} the {{Constrained Most Probable Explanation Task}} in {{Probabilistic Graphical Models}}},
  booktitle = {Proceedings of {{The}} 27th {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}}},
  author = {Arya, Shivvrat and Rahman, Tahrima and Gogate, Vibhav},
  year = {2024},
  month = apr,
  pages = {2791--2799},
  publisher = {PMLR},
  issn = {2640-3498},
  urldate = {2024-04-21},
}