Metraipolis › archive › dc_c911d2e005fe

Efficient Screening Designs for Expensive Black-box Models with Qualitative and Quantitative Factors — arXiv:2609.02087

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02087
License (as archived): CC0-1.0
Archived: 2026-10-17T05:17:00.060Z
SHA-256 of the archived text: 00a1b4ab6b7209c5a5c243c4e06d727a2294a6bb48d1cec8c09ea3418dd9094f
Stable URL of this page: https://metraipolis.com/documento/dc_c911d2e005fe

How to cite

Efficient Screening Designs for Expensive Black-box Models with Qualitative and Quantitative Factors — arXiv:2609.02087 — arXiv (en), https://arxiv.org/abs/2609.02087 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c911d2e005fe (Metraipolis archive dc_c911d2e005fe, sha256 00a1b4ab6b7209c5a5c243c4e06d727a2294a6bb48d1cec8c09ea3418dd9094f)

BibTeX

@misc{dcc911d2e005fe,
  title = {Efficient Screening Designs for Expensive Black-box Models with Qualitative and Quantitative Factors — arXiv:2609.02087},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02087},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c911d2e005fe; sha256 00a1b4ab6b7209c5a5c243c4e06d727a2294a6bb48d1cec8c09ea3418dd9094f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02087. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Computationally expensive black-box models often involve a large number of input factors with complex interactions and varying importance. Experimental design techniques can be used for quickly identifying the important factors, which can make the optimization of a complex computer model or the training of an expensive machine learning model more efficient. Existing screening designs for black-box models focus mainly on continuous factors, with the maximum one-factor-at-a-time (MOFAT) design being a recent example. In this work, we extend the design to incorporate multiple types of factors, including nominal, ordinal, and discrete-numeric. We first identify the properties leading to optimal screening, where qualitative and quantitative factors should be treated differently. Based on these properties, we propose practical algorithms to efficiently construct MOFAT designs for all types of factors. The usefulness of the design is demonstrated by both numerical experiments and an application to hyperparameter tuning in machine learning models.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-03 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.02087 — 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 2026-10-17T04:27:19.629Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).