Researchers have introduced a new framework for post-hoc Learning to Defer (L2D) by framing it through the lens of ideal distributions. This approach defines deferral based on the density-ratio between a model's and an expert's ideal distributions. The proposed method derives new loss functions for L2D scorers, enabling adjustable deferral rates without retraining and showing competitive and robust experimental results. AI
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IMPACT Introduces a novel theoretical framework for AI decision-making, potentially improving reliability in systems that require expert oversight.
RANK_REASON The cluster contains an academic paper detailing a new methodology in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]