Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
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Computer Science > Machine Learning
Title:Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
Abstract:Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context. This paper introduces Balanced Adaptive Prototype Selection (BAPS), a framework for constructing compact, information-preserving contexts for scalable TabPFN inference. Without modifying or retraining the pretrained model, BAPS jointly preserves representative structure, informative decision boundaries, local density, class balance, and feature-space diversity. Experiments on the million-row HIGGS and SUSY datasets show that 512 prototypes retain strong predictive performance and reliable calibration, corresponding to an approximately 1,953-fold context compression. All experiments were conducted on an Intel Core i7 CPU with 16 GB RAM and no GPU acceleration. These findings establish effective context construction as a practical mechanism for extending pretrained tabular foundation models to million-scale datasets.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.12989 [cs.LG] |
| (or arXiv:2608.12989v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12989
arXiv-issued DOI via DataCite (pending registration)
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