Title: ExpoTab: One-Step Mixed-Type Tabular Data Generation using Manifold Learning
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Tabular data Generation, Manifold Learning

Abstract: Fast generation of high-quality synthetic tabular data is critical for supporting latency-sensitive applications. This includes real-time fraud detection, where rapid model iteration depends on quick data synthesis, and large-scale system testing (e.g., e-commerce load simulation), where generators must produce massive volumes under strict time constraints to assess performance. However, current state-of-the-art (SOTA) tabular generators are inefficient, often requiring numerous denoising steps, and ineffective at handling high-cardinality categorical features and mixed-type data representations common in real-world datasets. We introduce an Exponential map method for Tabular data fast generation(ExpoTab), a geometric-aware flow model that addresses these challenges through two key innovations: (1) the ExpoTab-Encoder, a spherical encoder that projects categorical features onto optimized manifold coordinates, with quantile transformers to unify all features into a bounded representation space for stable optimization; and (2) the ExpoTab-Sampler, presenting a curved-path velocity field that leverages Riemannian geometry to model geodesics as one-step exponential maps, achieving one-step sampling. Extensive experiments across ten benchmarks demonstrate that ExpoTab outperforms existing methods in three key aspects: (a) our manifold-aware encoder achieves superior separability and flow matching performance; (b) the exponential map sampler accelerates generation by 4--10$\times$ while maintaining competitive fidelity; and (c) ExpoTab represents the first one-step generator effectively handling mixed-type tabular data, achieving $\sim$0.1s generation for 1M samples.