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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PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
SABRE calibrates a B-spline initial estimator against its simulated expectation, reducing finite-sample bias in semiparametric regression without inflating variance.
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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AI-Augmented Statistical Network Estimation with Proxy Gene Embeddings
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
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Calibrated Estimation and Inference for Semiparametric Regression Models
SABRE calibrates a B-spline initial estimator against its simulated expectation, reducing finite-sample bias in semiparametric regression without inflating variance.