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GOGGLE: Generative Modelling of Tabular Data By Learning Relational Structure

PyTorch implementation of ICLR'23 paper GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. Authors: Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar


Abstract

Generative modelling of tabular data entails a particular set of challenges, including heterogeneous relationships, limited number of samples, and difficulties in incorporating prior knowledge. This work introduces GOGGLE, a generative model that learns a relational structure underlying tabular data to better model variable dependencies, to introduce regularization, and to incorporate prior knowledge.

GOGGLE Overview Key components of GOGGLE Framework. 1. Simultaneous learning of relational structure $G_\phi$ and $F_\theta$ s.t. generative process respects relational structure. 2. Injection of prior knowledge and regularization on variable dependence. 3. Synthetic sample generated using $\hat{x} = F_\theta(z; G_\phi) :, : z\sim p_Z$.


Experiments

To setup the virtual environment and necessary packages, please run the following commands:

$ conda create --name goggle_env python=3.8
$ conda activate goggle_env

Clone this repository and navigate to the root directory:

$ git clone https://github.com/tennisonliu/goggle.git
$ cd goggle

Install the required modules:

$ pip install -r requirements.txt

Place dataset in exps/data, and see experiment notebooks with instructions in:

    1. exps/synthetic_data. for synthetic data generation
    1. exps/prior_knowledge/. for incorporating prior knowledge, and
    1. exps/ablation/. for ablation settings.

Citation

If our paper or code helped you in your own research, please cite our work as:

@inproceedings{liu2023goggle,
  title={GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure},
  author={Liu, Tennison and Qian, Zhaozhi and Berrevoets, Jeroen and van der Schaar, Mihaela},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

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