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Sagan

Paper

Density-Ratio Losses for Post-Hoc Learning to Defer

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AI summary

arXiv:2605. 19557v1 Announce Type: new Abstract: We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss.