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DECAF achieves near-optimal unlearning performance while maintaining efficiency, effectively neutralizing clustering attacks that threaten data privacy.
Explicitly modeling annotator bias and variability can significantly enhance uncertainty calibration in medical image segmentation without sacrificing accuracy.
Stop relying on a single "best" model: ToolSelect adaptively picks the right specialist model for each healthcare query, outperforming SOTA methods by learning from model behavior.
