[2603.23016] A Sobering Look at Tabular Data Generation via Probabilistic Circuits

Computer Science > Machine Learning arXiv:2603.23016 (cs) [Submitted on 24 Mar 2026] Title:A Sobering Look at Tabular Data Generation via Probabilistic Circuits Authors:Davide Scassola, Dylan Ponsford, Adrián Javaloy, Sebastiano Saccani, Luca Bortolussi, Henry Gouk, Antonio Vergari View a PDF of the paper titled A Sobering Look at Tabular Data Generation via Probabilistic Circuits, by Davide Scassola and 6 other authors View PDF HTML (experimental) Abstract:Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes. On this task, diffusion-based models are the current state-of-the-art (SotA) model class, achieving almost perfect performance on commonly used benchmarks. In this paper, we question the perception of progress for tabular data generation. First, we highlight the limitations of current protocols to evaluate the fidelity of generated data, and advocate for alternative ones. Next, we revisit a simple baseline -- hierarchical mixture models in the form of deep probabilistic circuits (PCs) -- which delivers competitive or superior performance to SotA models for a fraction of the cost. PCs are the generative counterpart of decision forests, and as such can natively handle heterogeneous data as well as deliver tractable probabilistic generation and inference. Finally, in a rigorous empirical analysis we show that the apparent saturation of progress for SotA models is largely due to the use of inadeq...