AugTab: Learnable Feature Augmentation for Low-Dimensional Tabular Data

Low-dimensional tabular datasets often exhibit noise, heterogeneity, and limited expressive capacity, while common pipelines rely on fixed expansions or manual feature engineering. We introduce AugTab, a modular framework that makes learnable feature augmentation a first-class component for tabular learning. At its core is a Feature Augmentation Layer (FAL) that expands inputs via complementary branches for nonlinear projections, explicit cross-interactions, and gated recombinations, all trained jointly with the downstream backbone. To address low-dimensional tabular constraints, AugTab includes regularizers for compute budget, feature stability, and robustness to distribution shift, enabling efficient and resilient deployments. The layer is architecture-agnostic and integrates with MLPs. Across 15 low-dimensional benchmarks spanning diverse domains, AugTab improves performance, achieves the best average rank across classification tasks, and attains the top result on all regression tasks, outperforming 54 classification baselines and 16 regression baselines, respectively. These results position learnable feature augmentation as a practical foundation for robust low-dimensional tabular learning.