  Generative modeling has recently seen many exciting developments with the
advent of deep generative architectures such as Variational Auto-Encoders (VAE)
or Generative Adversarial Networks (GAN). The ability to draw synthetic i.i.d.
observations with the same joint probability distribution as a given dataset
has a wide range of applications including representation learning, compression
or imputation. It appears that it also has many applications in privacy
preserving data analysis, especially when used in conjunction with differential
privacy techniques. This paper focuses on synthetic data generation models with
privacy preserving applications in mind. It introduces a novel architecture,
the Composable Generative Model (CGM) that is state-of-the-art in tabular data
generation. Any conditional generative model can be used as a sub-component of
the CGM, including CGMs themselves, allowing the generation of numerical,
categorical data as well as images, text, or time series. The CGM has been
evaluated on 13 datasets (6 standard datasets and 7 simulated) and compared to
14 recent generative models. It beats the state of the art in tabular data
generation by a significant margin.
