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Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks — arXiv:2611.04848

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04848
License (as archived): CC0-1.0
Archived: 2026-11-04T12:28:38.794Z
SHA-256 of the archived text: f7257672c87d0542356c68f180071e12b4aeacb0f07d6c20a0bef267ed6ce273
Stable URL of this page: https://metraipolis.com/documento/dc_378d398992ef

How to cite

Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks — arXiv:2611.04848 — arXiv (en), https://arxiv.org/abs/2611.04848 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_378d398992ef (Metraipolis archive dc_378d398992ef, sha256 f7257672c87d0542356c68f180071e12b4aeacb0f07d6c20a0bef267ed6ce273)

BibTeX

@misc{dc378d398992ef,
  title = {Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks — arXiv:2611.04848},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04848},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_378d398992ef; sha256 f7257672c87d0542356c68f180071e12b4aeacb0f07d6c20a0bef267ed6ce273},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04848. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Variable selection is an important task for interpreting potentially complex systems in fields such as biology, business, and sociology. The importance of variable selection has attracted considerable attention in statistics and machine learning. In this paper, we are mainly interested in model-agnostic variable selection for identifying variables that have general nonlinear dependence on the response. We propose MambaPFN, short for Model-Agnostic Markov BlAnket selection with Prior-data Fitted Networks, which is built upon the Transformer, a building block of many popular large language models. Specifically, we consider a functional-mapping perspective on variable-selection learning, in which the Transformer is treated as a learnable mapping from a dataset to its relevant variables. To learn this mapping, MambaPFN introduces three new components: (i) a faithful, rich, and scalable scheme for generating pretraining data, (ii) an objective that targets each variable's posterior inclusion probability, and (iii) a modified Transformer architecture tailored to variable selection. Once pretrained, MambaPFN is applied to new datasets without parameter updates. Empirically, MambaPFN achieves desirable variable-selection performance in simulation studies and exhibits a scaling law---more pretraining datasets and larger models can continuously improve its performance. Applied to a Facebook comments dataset, MambaPFN selects the fewest variables and achieves the highest computational efficiency and predictive performance, improving upon the best baselines by about 20%.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Methodology (stat.ME); Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2611.04848 — 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 2026-11-04T12:26:47.181Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).