What is the impact of latent space dimensionality in VAEs versus diffusion models on the transferability of ad

Abstract

Abstract: The rising use of machine learning in various fields requires robust methods to create synthetic tabular data. Data should preserve key characteristics while addressing data scarcity challenges. Current approaches based on Generative Adversarial Networks, such as the state-of-the-art CTGAN model, struggle with the complex structures inherent in tabular data. These data often contain both continuous and discrete features with non-Gaussian distributions. Therefore, we propose a novel Variational Autoencoder (VAE)-based model that addresses these limitations. Inspired by the TVAE model, our appro

Research Question

What is the impact of latent space dimensionality in VAEs versus diffusion models on the transferability of adversarial attacks in tabular foundation model evaluations?

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