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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The paper introduces modal calibration for nominal outcomes, distinguishes full/partial/average calibration, demonstrates logical independence of double PIT calibration for discrete outcomes, and generalizes calibration results expressed via functionals of predictive distributions.
Augments the energy score objective for sample-based generative models with a differentiable decision loss that is itself a proper scoring rule, yielding targeted improvements on cost-sensitive regions in synthetic and real tasks.
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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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Hierarchies of Calibration: Classification meets Regression
The paper introduces modal calibration for nominal outcomes, distinguishes full/partial/average calibration, demonstrates logical independence of double PIT calibration for discrete outcomes, and generalizes calibration results expressed via functionals of predictive distributions.
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Decision-Aware Training for Sample-Based Generative Models
Augments the energy score objective for sample-based generative models with a differentiable decision loss that is itself a proper scoring rule, yielding targeted improvements on cost-sensitive regions in synthetic and real tasks.