Learning to Defer with an Uncertain Rejector via Conformal Prediction

Introduces an uncertainty-aware rejector for learning-to-defer systems. For applied scientist review, this TMLR paper is most relevant as evidence of rigorous evaluation for human-AI collaboration, calibration, robustness under distribution shift, and decision quality beyond raw model accuracy.