arXiv Machine Learning August 24, 2026 By Siyuan Tang, Gongjun Xu, Ji Zhu

Conditional-Independence-Regularized Distributional Autoencoders for Mixed-Type Data

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The paper introduces Conditional-Independence-Regularized Distributional Autoencoders, a framework for learning low-dimensional representations of mixed-type data that includes both numerical and categorical variables. It uses an energy-score objective for numerical variables, a likelihood objective for categorical variables, and an auxiliary conditional independence regularization term to capture dependencies between variable types. The authors provide theoretical analysis and demonstrate that the method improves categorical distribution recovery, achieves competitive overall conditional distribution recovery, and preserves mixed-type dependence structure on synthetic and real-world datasets.

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