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Replicable Conformal Prediction — arXiv:2608.23638

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.23638
License (as archived): CC0-1.0
Archived: 2026-10-18T06:36:31.506Z
SHA-256 of the archived text: 4e9e1c762cbfe5ce9faca12b65717736145a25c260f2cd02827df089e12cef8f
Stable URL of this page: https://metraipolis.com/documento/dc_602b6e2ebfd2

How to cite

Replicable Conformal Prediction — arXiv:2608.23638 — arXiv (en), https://arxiv.org/abs/2608.23638 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_602b6e2ebfd2 (Metraipolis archive dc_602b6e2ebfd2, sha256 4e9e1c762cbfe5ce9faca12b65717736145a25c260f2cd02827df089e12cef8f)

BibTeX

@misc{dc602b6e2ebfd2,
  title = {Replicable Conformal Prediction — arXiv:2608.23638},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.23638},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_602b6e2ebfd2; sha256 4e9e1c762cbfe5ce9faca12b65717736145a25c260f2cd02827df089e12cef8f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.23638. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration threshold inherits the randomness of the data. Wherever deployments must be audited, cached, or approved across sites, this instability is costly: no one can verify that two calibrations produced the same object. We ask two questions: when can independent calibrations yield the identical classifier, and what must that agreement cost? Perfect agreement is impossible, since a procedure that almost always returns one fixed answer cannot remain valid for every distribution, and exact agreement through shared randomness forces the procedure to ignore its data. Sharing a single random seed and rounding the calibrated threshold up to a coarse shared grid resolves the tension: the deployed classifier becomes identical across analysts with any desired probability, coverage guarantees survive, and the price is a quantified increase in set size and calibration data. Matching lower bounds show that no threshold method can pay less, and the method's one tuning constant vanishes asymptotically. Without any shared seed, a fixed grid still confines all analysts to two adjacent classifiers, and no method does better. Replicability also blocks gaming: selecting the most favorable of many recalibrations barely moves a replicable classifier, while the same selection silently undercovers standard conformal prediction. Experiments on real ImageNet outputs, a four-hospital site split, and four language-model families match the theory, including the measured sample-cost frontier.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.23638 — 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 2026-10-18T06:28:52.301Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).