CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
This addresses a bottleneck in generative modeling for mixed-type tabular datasets, particularly for imbalanced and high-cardinality features, though it is incremental as it builds on existing VAE frameworks.
The paper tackled the problem of generative modeling for high-cardinality categorical features in tabular data by introducing CardiCat, a variational autoencoder that uses regularized dual encoder-decoder embeddings, resulting in high-quality synthetic data with a smaller parameter space than competing methods.
High-cardinality categorical features are a common characteristic of mixed-type tabular datasets. Existing generative model architectures struggle to learn the complexities of such data at scale, primarily due to the difficulty of parameterizing the categorical features. In this paper, we present a general variational autoencoder model, CardiCat, that can accurately fit imbalanced high-cardinality and heterogeneous tabular data. Our method substitutes one-hot encoding with regularized dual encoder-decoder embedding layers, which are jointly learned. This approach enables us to use embeddings that depend also on the other covariates, leading to a compact and homogenized parameterization of categorical features. Our model employs a considerably smaller trainable parameter space than competing methods, enabling learning at a large scale. CardiCat generates high-quality synthetic data that better represent high-cardinality and imbalanced features compared to competing VAE models for multiple real and simulated datasets.