GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
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Computer Science > Machine Learning
Title:GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
Abstract:We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones. We introduce Graph-guided Ordering with Local Refinement (GO-LR), show its equivalence to weighted Minimum Linear Arrangement, and interpret the practical solver as a TSP-path-style surrogate. We propose GOTabPFN,which builds on GO-LR, and a Neuro-Inspired Subunit Compression (NSC) unit to pool locally adjacent ordered features into meta-features, yielding a compact representation that makes TabPFN-style prediction practical in HDLSS regimes. Across tabular benchmarks, GOTabPFN improves stability and accuracy under tight token budgets.
| Comments: | Accepted to the 43rd International Conference on Machine Learning (ICML 2026). Code and resources are available at: GitHub: this https URL PyPI: this https URL Project webpage: this https URL Hugging Face Space: this https URL and this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2606.05441 [cs.LG] |
| (or arXiv:2606.05441v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.05441
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Al Zadid Sultan Bin Habib [view email][v1] Wed, 3 Jun 2026 21:03:33 UTC (11,665 KB)
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