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Introduces PEBS, a per-rater empirical-Bayes shrinkage estimator for calibrating reward models in RLHF, reducing within-user RMSE by over 8.5% on PRISM and over 9.6% on PluriHarms.
This paper introduces SLC (State-space Logit Correction), which corrects per-item logit bias in knowledge tracing models using empirical-Bayes shrinkage via a Kalman smoother, improving AUC beyond global calibration techniques.
This paper introduces an empirical Bayes conformal prediction framework that uses r-values to incorporate score variability into nonconformity scores, improving ranking stability and reducing set size while preserving coverage for vision and language models.