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Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection — arXiv:2609.28522

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.28522
License (as archived): CC0-1.0
Archived: 2026-10-08T05:26:20.049Z
SHA-256 of the archived text: d3fb344191fb23653e382964c9bf4b08f483e321e785e5ff426aae5577e717e5
Stable URL of this page: https://metraipolis.com/documento/dc_fe0dbe80d581

How to cite

Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection — arXiv:2609.28522 — arXiv (en), https://arxiv.org/abs/2609.28522 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fe0dbe80d581 (Metraipolis archive dc_fe0dbe80d581, sha256 d3fb344191fb23653e382964c9bf4b08f483e321e785e5ff426aae5577e717e5)

BibTeX

@misc{dcfe0dbe80d581,
  title = {Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection — arXiv:2609.28522},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.28522},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fe0dbe80d581; sha256 d3fb344191fb23653e382964c9bf4b08f483e321e785e5ff426aae5577e717e5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.28522. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Modern conformal forecasting systems often maintain several adaptive pipelines that differ in base forecasters, conformity scores, calibration windows, and update rules. Comparing them is difficult because coverage is a hard constraint, whereas efficiency should be optimized only among feasible pipelines. We formulate this problem as sequential inference for a stochastic constrained argmin. At each time, the target is the set of minimum-cost pipelines satisfying multiple prefix-average conditional miscoverage constraints. We introduce Coverage-Constrained Sequential Model Confidence Sets (CC-SMCS), which separate certifiably feasible, possibly feasible, and possibly constrained-optimal pipelines. Using simultaneous martingale confidence sequences, CC-SMCS projects a rectangular confidence region onto the constrained argmin and admits an exact closed-form rule. With probability at least $1-\delta$, it contains every constrained-optimal pipeline simultaneously over all times. This finite-sample guarantee requires no stationarity or mixing assumptions and remains valid under data-dependent stopping. We also establish an impossibility result for safe certification at the coverage boundary and extend the construction to delayed multi-horizon feedback and outcome-dependent efficiency objectives.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-25 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.28522 — 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 2026-10-08T04:50:39.530Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).