An analytical expected-gain score from calibrated posteriors and classwise reliability estimates decides escalation in VFL, improving communication-accuracy trade-off over baselines.
Machine learning with a reject option: A survey
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SHRUG-FM fuses geophysical OOD detection, embedding-space OOD detection, and predictive uncertainty via a shallow decision tree to let foundation models abstain from unreliable outputs on burn scar, flood, and landslide tasks.
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Expected Gain-based Escalation in Vertical Federated Learning
An analytical expected-gain score from calibrated posteriors and classwise reliability estimates decides escalation in VFL, improving communication-accuracy trade-off over baselines.
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SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
SHRUG-FM fuses geophysical OOD detection, embedding-space OOD detection, and predictive uncertainty via a shallow decision tree to let foundation models abstain from unreliable outputs on burn scar, flood, and landslide tasks.