TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
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Computer Science > Machine Learning
Title:TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
Abstract:Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity. However, recent approaches with either cell-based architectures or retrieval have sacrificed efficiency for raw performance, restricting their utility in situations where compute is limited or inference speed is crucial. We adopt an alternate approach, sticking with row-based attention while incorporating long context pre-training to eliminate the need for retrieval. By combining this with architectural improvements and SSL pre-training on a newly-sourced, larger corpus of real data results, we present TabDPT-Turbo, a model that provides comparable default performance to TabDPT v1.1 on TabArena-Lite, CC18, and CTR23, at orders of magnitude faster. In our experiments, TabDPT-Turbo is the fastest model overall among leading foundation models. We have released the new model as TabDPT v1.2 at this https URL.
| Comments: | Presented as a poster at the non-archival ICML workshop on Foundation Models for Structured Data (FMSD) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.01400 [cs.LG] |
| (or arXiv:2608.01400v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.01400
arXiv-issued DOI via DataCite (pending registration)
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