TabTailor: Tail and Ordinal-Aware Mixed-Type Diffusion Model for Tabular Data Synthesis

Generative models have significantly enhanced the quality of synthetic tabular data and have been applied to diverse real-world domains. However, current tabular generative models lack the ability to preserve tails of numerical features and they consider ordinal features as unordered. Therefore, preserving heavy-tailed numerical distribution and the ordering of ordinal attributes remains challenging. We propose TabTailor, a mixed-type hybrid diffusion framework that aims to preserve the tail and ordinal semantics of the tabular data. For numerical variables, TabTailor uses an adaptive semiparametric marginal transform to model each feature’s tails before the fixed cosine VP diffusion process. The categorical variables employ masking budgets and learnable per feature-and-state-dependent cor- ruption schedules. TabTailor also uses a cumulative semantic embedding and a strictly increasing rank coordinate to represent ordinal attributes as an embedding, called CMOE (Cumulative Monotonic Ordinal Embedding). A denoising back-bone is implemented using a bidirectional transformer for modelling numerical, nominal and ordinal side information jointly, and a state-consistent mixed-type stochastic-restart sampler is used to mitigate decoding error accumulation. The denoiser and the learnable corruption schedule are trained jointly under one diffusion objective. TabTailor performs better than state-of-the-art baselines in seven benchmark datasets and ten metrics while maintaining overall preservation of the numerical tails and ordinal structure.