Tydra: An Efficient Hybrid Model for Tabular Data
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Computer Science > Machine Learning
Title:Tydra: An Efficient Hybrid Model for Tabular Data
Abstract:Transformer-based tabular foundation models such as TabPFN achieve strong predictive performance but incur quadratic computational cost with context length. On the other hand, subquadratic SSM-based alternatives such as Hydra trade away accuracy for efficiency. To balance both, we introduce Tydra, a hybrid Transformer-State Space Model (SSM) architecture for tabular in-context learning that interleaves attention and SSM layers. Across 30 OpenML datasets, Tydra reduces inference time by 30% relative to TabPFN while retaining much of its predictive performance. Tydra also outperforms an approximately ten-times-larger Hydra model while providing faster inference. The results indicate that hybrid architectures are a promising direction for tabular foundation models.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.21199 [cs.LG] |
| (or arXiv:2608.21199v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.21199
arXiv-issued DOI via DataCite (pending registration)
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