Metraipolis › archive › dc_ffbe7d06afa9

Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data — arXiv:2608.12989

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.12989
License (as archived): CC0-1.0
Archived: 2026-10-19T04:27:31.764Z
SHA-256 of the archived text: b5632bd1e8f09cf95ce5fa18a39a073360e24129d778145f178c90022e5831dc
Stable URL of this page: https://metraipolis.com/documento/dc_ffbe7d06afa9

How to cite

Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data — arXiv:2608.12989 — arXiv (en), https://arxiv.org/abs/2608.12989 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ffbe7d06afa9 (Metraipolis archive dc_ffbe7d06afa9, sha256 b5632bd1e8f09cf95ce5fa18a39a073360e24129d778145f178c90022e5831dc)

BibTeX

@misc{dcffbe7d06afa9,
  title = {Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data — arXiv:2608.12989},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.12989},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ffbe7d06afa9; sha256 b5632bd1e8f09cf95ce5fa18a39a073360e24129d778145f178c90022e5831dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.12989. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-14 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.12989 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T04:20:35.842Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).