  Despite the prevalence of images and texts in machine learning, tabular data
remains widely used across various domains. Existing deep learning models, such
as convolutional neural networks and transformers, perform well however demand
extensive preprocessing and tuning limiting accessibility and scalability. This
work introduces an innovative approach based on a structured state-space model
(SSM), MambaTab, for tabular data. SSMs have strong capabilities for
efficiently extracting effective representations from data with long-range
dependencies. MambaTab leverages Mamba, an emerging SSM variant, for end-to-end
supervised learning on tables. Compared to state-of-the-art baselines, MambaTab
delivers superior performance while requiring significantly fewer parameters,
as empirically validated on diverse benchmark datasets. MambaTab's efficiency,
scalability, generalizability, and predictive gains signify it as a
lightweight, "plug-and-play" solution for diverse tabular data with promise for
enabling wider practical applications.
