Metraipolis › archive › dc_718bcbe404fc

Sample-Efficient Generative Conformal Prediction — arXiv:2610.11349

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.11349
License (as archived): CC0-1.0
Archived: 2026-10-09T06:23:18.061Z
SHA-256 of the archived text: b991c5d4f90e447f51deb287390f2145bd26f9ad1c4348107fa6a980ad286c83
Stable URL of this page: https://metraipolis.com/documento/dc_718bcbe404fc

How to cite

Sample-Efficient Generative Conformal Prediction — arXiv:2610.11349 — arXiv (en), https://arxiv.org/abs/2610.11349 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_718bcbe404fc (Metraipolis archive dc_718bcbe404fc, sha256 b991c5d4f90e447f51deb287390f2145bd26f9ad1c4348107fa6a980ad286c83)

BibTeX

@misc{dc718bcbe404fc,
  title = {Sample-Efficient Generative Conformal Prediction — arXiv:2610.11349},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.11349},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_718bcbe404fc; sha256 b991c5d4f90e447f51deb287390f2145bd26f9ad1c4348107fa6a980ad286c83},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.11349. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative conformal prediction builds uncertainty sets from samples of a conditional generator, which are efficient only when the samples represent the response distribution well. This can require many samples, each of which can be costly, as in large diffusion models and scientific simulators, so the sampling budget must be used efficiently. Existing methods draw the same number of samples at every input, wasting samples where the response distribution is simple and undersampling where it is complex, which inflates sets and leaves those inputs under-covered. We propose CASA (Conformal Adaptive Sample Allocation), which characterizes the marginal value of an additional sample and allocates samples across inputs to minimize the expected set size subject to marginal coverage and an average sampling budget. Theoretical analysis shows that adaptive allocation yields smaller sets than a fixed count at the same budget: a missed mode forces a radius that spans the gap between modes, and even oracle radius cannot compensate for it. On synthetic and real tasks, CASA produces substantially smaller sets at the same budget, often improves conditional coverage, and complements existing radius-adaptive methods.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2610.11349 — 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 2026-10-09T05:50:31.211Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).