PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Function-on-scalar regression captures time-varying effects of physical activity interventions on daily trajectories better than FPCA followed by scalar regression, as shown in the STEP UP study.
SABRE is a simulation-based bias correction framework that reduces finite-sample bias for the parametric component and dispersion parameter in semiparametric regression models, with asymptotic bias reduction without variance inflation shown for generalized partially linear models.
citing papers explorer
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression
Function-on-scalar regression captures time-varying effects of physical activity interventions on daily trajectories better than FPCA followed by scalar regression, as shown in the STEP UP study.
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Bias Correction for Semiparametric Regression Models
SABRE is a simulation-based bias correction framework that reduces finite-sample bias for the parametric component and dispersion parameter in semiparametric regression models, with asymptotic bias reduction without variance inflation shown for generalized partially linear models.