AI RESEARCH
Parameter-Free and Group Conditional Online Conformal Prediction
arXiv CS.LG
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ArXi:2606.00419v1 Announce Type: cross Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data may not be exchangeable). Online conformal prediction (OCP) methods address this issue at the expense of either (i) group-wise error control or (ii) learning-rate independent implementation. Group-conditional coverage is essential for fairness across different collections of data points and for providing finer UQ guarantees.