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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Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
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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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Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.