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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2 Pith papers cite this work. Polarity classification is still indexing.
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In a Gaussian single-index model, neural reward models recover the hidden direction for β1 above an O(1) threshold and provide tilted-policy value-gap bounds for label-weighted and surrogate-weighted exponential fits.
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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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How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis
In a Gaussian single-index model, neural reward models recover the hidden direction for β1 above an O(1) threshold and provide tilted-policy value-gap bounds for label-weighted and surrogate-weighted exponential fits.