Hyperbolic Manifold Constrained Tabular Neural Network
Tabular prediction is central to a wide range of real-world applications. Tabular data typically contain heterogeneous features as well as rich and complex relational information that can imply a latent structural manifold. Hyperbolic geometry can help capture complex structural relations in data. However, most existing tabular prediction models are constructed and optimized in Euclidean space. How to incorporate hyperbolic geometry into supervised tabular learning remains underexplored. We propose \textbf{HTNN}, a supervised hyperbolic manifold constrained tabular neural network for tabular prediction. HTNN consists of a hyperbolic feature-value representation layer for heterogeneous cat...
| Authors | Tian Li, Huizhi Liang, Lucy Robinson et al. |
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| Primary category | cs.LG |
| Published | 23 June 2026 |
| Last updated at source | 28 September 2026 |
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