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LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction — arXiv:2609.36396

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.36396
License (as archived): CC0-1.0
Archived: 2026-10-07T17:32:37.266Z
SHA-256 of the archived text: 025df8717d323781b35fa47b42aca032ae3602726b76f0f51cf7e6b2bbff802f
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How to cite

LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction — arXiv:2609.36396 — arXiv (en), https://arxiv.org/abs/2609.36396 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_641e2ac641ec (Metraipolis archive dc_641e2ac641ec, sha256 025df8717d323781b35fa47b42aca032ae3602726b76f0f51cf7e6b2bbff802f)

BibTeX

@misc{dc641e2ac641ec,
  title = {LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction — arXiv:2609.36396},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.36396},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_641e2ac641ec; sha256 025df8717d323781b35fa47b42aca032ae3602726b76f0f51cf7e6b2bbff802f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.36396. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge. One popular type of interpretation is leave-one-covariate-out (LOCO) feature importance, while prior LOCO inference methods often require data-splitting or model-refitting. A recent ensemble framework, LOCO-MP, addresses these challenges using minipatches that subsample both observations and features, but massive feature subsampling can hurt prediction in high-dimensional sparse settings. Motivated by this limitation, we consider minipatch ensembles with adaptive feature sampling guided by LOCO importance, and propose LOCO-AdaMP, which enables free LOCO inference for the resulting adaptive minipatch ensemble. We show that LOCO-AdaMP yields substantially improved predictive models while retaining asymptotically valid feature importance inference without data-splitting, despite the complex dependence between the adaptive sampling distribution and the LOCO importance statistics. Our analysis relies on a careful leave-two-out perturbation bound for the iteratively updated sampling probabilities together with the stability of LOCO scores induced by observation subsampling. Empirical results on synthetic and real datasets demonstrate advantages of LOCO-AdaMP over existing methods in predictive performance, inferential power, and stability. Overall, LOCO-AdaMP provides a flexible ensemble framework (agnostic to base models) that delivers both strong predictive performance and asymptotically valid, powerful feature importance inference for regression.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.36396 — 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 2026-10-07T16:59:48.490Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).