Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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Generalized conformal predictive systems are extended to non-exchangeable settings under distributional shifts via permutation weights and robust weight-uncertainty boxes with finite-sample or asymptotic guarantees.
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Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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Generalized Conformal Predictive Systems Under Distributional Shifts
Generalized conformal predictive systems are extended to non-exchangeable settings under distributional shifts via permutation weights and robust weight-uncertainty boxes with finite-sample or asymptotic guarantees.