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About this paper
Priyanka Siddappa, Muntaqim Ahmed Raju, Shifat Haider Al Amin, Ruizhe Ma
The authors test whether missing calibration answers could change a personalized model’s current rating prediction. Their method preserves the partial predictor and uses exact worst-case margins to test every missing-answer completion without retraining. In clinician ratings, an affine partial update raised log loss by 0.0578 nats versus Observed Fisher, while both rules certified the same targets.
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