distvae-tabular 0.1.0

DistVAE Implementation Package for Synthetic Data Generation

DistVAE-Tabular

DistVAE is a novel approach to distributional learning in the VAE framework, focusing on accurately capturing the underlying distribution of the observed dataset through a nonparametric CDF estimation.

We utilize the continuous ranked probability score (CRPS), a strictly proper scoring rule, as the reconstruction loss while preserving the mathematical derivation of the lower bound of the data log-likelihood. Additionally, we introduce a synthetic data generation mechanism that effectively preserves differential privacy.

1. Installation

Install using pip:

pip install distvae-tabular

2. Usage

from distvae_tabular import distvae

distvae.DistVAE # DistVAE model
distvae.generate_data # generate synthetic data

• See example.ipynb for detailed example with loan dataset.
  • Link for download loan dataset: https://www.kaggle.com/datasets/teertha/personal-loan-modeling

Citation

If you use this code or package, please cite our associated paper:

@article{an2024distributional,
  title={Distributional learning of variational AutoEncoder: application to synthetic data generation},
  author={An, Seunghwan and Jeon, Jong-June},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}