Regression with Deferral to Multiple Experts: A Principled Approach with Strong Consistency Guarantees

This work introduces a novel framework for regression with deferral, where the learner can choose to defer predictions to multiple experts. The authors present a comprehensive analysis for both single-stage and two-stage scenarios, deriving new surrogate loss functions with strong (H,R)-consistency bounds. Their versatile framework accommodates multiple experts, arbitrary bounded regression losses, and both instance-dependent and label-dependent costs.