AI RESEARCH
Online Conformal Prediction with Corrupted Feedback
arXiv CS.LG
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ArXi:2605.20515v1 Announce Type: new Modern artificial intelligence systems require calibrated uncertainty estimates that remain reliable in sequential and non-stationary environments. Online conformal prediction (OCP) addresses this challenge through adaptively updated prediction sets that provide deterministic long-run miscoverage guarantees. These guarantees, however, hinge on the assumption of perfect feedback about the coverage of past prediction sets.