A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis
Rongchao Zhang,
Yiwei Lou,
Dexuan Xu
et al.
Abstract:The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learnin… Show more
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