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Available Guardrails: Certifying Selective Prediction across ML Systems

Paper recorded by Signals 4 on 2026-09-18 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.LG · 机器学习 · first seen 2026-09-21

Abstract

A selective predictor acts as a safety gate: it returns an output only when the prediction appears sufficiently trustworthy. Deployments increasingly require this reliability to be certified at a target precision for every reporting unit of interest, such as a tool, policy label, or patient subgroup. The main difficulty is often not whether a granted certificate is valid, but whether finite calibr

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#5 most recent of 235 cs.LG papers we have recorded · ↑ newer: Particle Competition and Cooperation for Robust Graph Convolutional Ne · ↓ older: $λ$-Controlled GRPO: Turning Flow-Matching Ratio Instability into a Bu
Cite this page: Available Guardrails: Certifying Selective Prediction across ML Systems: the #5 most recent of 235 cs.LG papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/available-guardrails-certifying-selective-prediction-across-ml-systems.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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