Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
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2 Pith papers cite this work. Polarity classification is still indexing.
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spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
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From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings
Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
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Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.