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Early Time Classification with Accumulated Accuracy Gap Control
This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classification.
Achieving Risk Control in Online Learning Settings
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage of confidence intervals, false negative rate, or F1 score -- in the online setting.
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