CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data

Lee Carlin, Yuval Benjamini

Keywords: embedding, VAE, tabular, regularization, high-cardinality, categorical, imbalance, mixed, heterogeneous, layers, Generative, model

TL;DR: CardiCat introduces a regularized dual encoder-decoder embedding VAE architecture to efficiently learn high-cardinality and imbalanced tabular data.

Abstract: 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.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 9991