arXiv Machine Learning
Jun 9

A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control

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
arXiv Machine Learning
Sep 21

Available Guardrails: Certifying Selective Prediction across ML Systems

The paper introduces a method to certify selective prediction in machine learning systems by computing the availability of safety gates through exact-binomial inversion and dynamic programming. It demonstrates that a truth-informed planner can significantly improve mean coverage over naive approaches, and that reallocating error budgets further enhances coverage across diverse applications such as LLM tool‑calling, content moderation, lesion classification, and recommendation. The study highlights the importance of planning and finite‑sample estimation in ensuring reliable, granular deployment of selective predictors.

By Parivesh Priye, Yufeng Wang, Haibin Ling, Michael Chaykowsky