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This monograph presents a unified statistical framework for reliable post-training hyperparameter selection, centered on the learn-then-test (LTT) paradigm, that enables the choice of hyperparameters that provably satisfy application-specific reliability requirements.
This work develops OCP with queries (OCPQ) by adapting the label efficient forecaster of Cesa-Bianchi, Lugosi, and Stoltz (2004) to the authors' setting, and develops OCP with queries (OCPQ) with queries in a way that encourages the learner to output small prediction sets while ensuring that the correct label is covere...
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