Generalized conformal predictive systems are extended to non-exchangeable settings under distributional shifts via permutation weights and robust weight-uncertainty boxes with finite-sample or asymptotic guarantees.
Angelopoulos, Jennifer Listgarten, and Michael I
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
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Generalized Conformal Predictive Systems Under Distributional Shifts
Generalized conformal predictive systems are extended to non-exchangeable settings under distributional shifts via permutation weights and robust weight-uncertainty boxes with finite-sample or asymptotic guarantees.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.