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Online selective conformal inference: adaptive scores, convergence rates and optimality
In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Candès (2021). For any given point-prediction algorithm, their method (ACI) produces a conformal prediction set with an average miscoverage getting close to a prespecified level $α$ for a long
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