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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TRACE models post-click feedback as trajectories to dynamically refine conversion posteriors and uses a reliability-gated retrospective completer to guide early incomplete samples, outperforming prior delay modeling and reweighting approaches.
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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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Follow the TRACE: Exploiting Post-Click Trajectories for Online Delayed Conversion Rate Prediction
TRACE models post-click feedback as trajectories to dynamically refine conversion posteriors and uses a reliability-gated retrospective completer to guide early incomplete samples, outperforming prior delay modeling and reweighting approaches.