arXiv:2606. 19147v1 Announce Type: cross Abstract: This paper develops local certificates for population-risk increments around a current model.
By Mingzhi Song
arXiv:2606. 19147v3 Announce Type: replace-cross Abstract: How can training data be used to compare local updates to the current model, choose an update, and retain valid bounds for the selected update's population-risk change?
By Mingzhi Song
The paper introduces a distribution‑free certification layer that can be applied to any crash‑severity prediction model without modifying the model itself. It provides guarantees for ordinal outcomes, per‑class validity, transfer of coverage to unobserved severities, and one‑sided certificates under deployment shift, all grounded in a functional of the true data law. The framework is evaluated on 5.2 million Texas records, demonstrating a model‑independent lower bound on set width for vulnerable road users and is released as an open‑source package with theorem‑level tests.
By Amir Rafe, Subasish Das
The paper investigates the reliability of machine‑parsed statutes by developing a passive survival certificate for the Duquenne‑Guigues implication basis of extracted legal contexts. It measures inter‑extractor disagreement, runs 1,000 Monte‑Carlo trials, and certifies an implication only when a one‑sided Wilson 95% lower bound on survival reaches 0.95, providing premise spans and minimal counterexamples. Applied to 29,365 Missouri sections and 502 Indian central‑Act sections, the method passes a held‑out gate for many statute families, yet a global error model shows that 93.2% of held‑out chapters fall below the informativeness floor, attributing this to calibration‑rate transfer rather than selection bias.
By Surya Saka
arXiv:2606. 08517v1 Announce Type: new Abstract: Selective predictors answer on confident inputs and abstain elsewhere; deploying one safely needs a single finite-sample certificate that simultaneously upper-bounds the selected risk, lower-bounds the acceptance probability $\pacc$ above a floor $\pmin$, and lower-bounds the deployment utility.
By Xiaoli Yu, Jiamiao Liu
Selective-risk certificates promise that accepted outputs meet a declared error target. We develop Fed-SRC, a score-agnostic certificate for federated, differentially private, adaptively monitored retrieval-augmented generation.