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Federal University of São Carlos
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Marginal conformal guarantees often mask severe error spikes on hard examples, but inverting localized risk curves delivers tight conditional error control without sacrificing distribution-free finite-sample validity.
Stop relying on heuristics: LOCUS offers a distribution-free, per-input loss-scale reliability score that lets you directly quantify and control the risk of large losses from your ML models.
