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Conformal Policy Learning with Distribution-Free Safety Guarantees

Paper recorded by Signals 4 on 2026-09-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.LG · 机器学习 · first seen 2026-09-16

Abstract

Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the average outcomes alone may not be sufficient: decision makers may also seek to protect individuals from harm, in line with the Hippocratic principle of ``do no harm.'' In this paper, we propose \text

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#39 most recent of 215 cs.LG papers we have recorded · ↑ newer: Goal-oriented probabilistic forecasting for dynamic PRB allocation in · ↓ older: Same Flow, Different Paths: Variance Reduction in Flow Matching
Cite this page: Conformal Policy Learning with Distribution-Free Safety Guarantees: the #39 most recent of 215 cs.LG papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/conformal-policy-learning-with-distribution-free-safety-guarantees.html
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