Develops the first adversarial robustness framework for one-stage learning-to-defer, including cost-sensitive surrogate losses and theoretical consistency guarantees for classification and regression.
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Adversarial Robustness in One-Stage Learning-to-Defer
Develops the first adversarial robustness framework for one-stage learning-to-defer, including cost-sensitive surrogate losses and theoretical consistency guarantees for classification and regression.