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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A functional Cox model is developed for interval-censored data using penalized maximum likelihood estimation via an EM algorithm, with proofs of consistency, asymptotic normality, and semiparametric efficiency, plus a global test for the functional covariate effect.
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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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Functional Cox model for interval-censored data
A functional Cox model is developed for interval-censored data using penalized maximum likelihood estimation via an EM algorithm, with proofs of consistency, asymptotic normality, and semiparametric efficiency, plus a global test for the functional covariate effect.