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 finite-horizon mixture cure model is introduced that divides populations based on event occurrence within a specific period, yielding different variable significance and seasonal insights than infinite-horizon versions when applied to Mercari transaction data.
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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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A Finite-Horizon Mixture Cure Model with Application to Online Flea Market Data
A finite-horizon mixture cure model is introduced that divides populations based on event occurrence within a specific period, yielding different variable significance and seasonal insights than infinite-horizon versions when applied to Mercari transaction data.