Conformal Prediction for Dyadic Regression Under Complex Missingness
Researchers have developed a new framework for conformal prediction in dyadic regression, specifically addressing complex missing data scenarios. The theoretical advancements include establishing super-uniformity under weaker invariance conditions and handling samples that are random subsets of the index set. The proposed methods also offer asymptotic validity for weighted conformal prediction even under missing-not-at-random assumptions, a significant theoretical contribution. AI