New Framework for Conformal Prediction in Dyadic Regression with Missing Data
Researchers have developed a theoretical framework for conformal prediction—a statistical method for quantifying uncertainty—applied to dyadic regression problems where data is missing in complex patterns. The work extends existing theory by handling cases where samples are random subsets of an index set and introduces several new prediction procedures for jointly exchangeable arrays. This advances the mathematical foundations of uncertainty quantification in network and relational data analysis.
The paper presents a comprehensive framework for applying conformal prediction to dyadic regression under complex missingness mechanisms. Key theoretical contributions include establishing super-uniformity of conformal prediction under weaker conditions than exchangeability, and a novel bijection argument that handles the case where the sample itself is a random subset—a scenario not previously covered by theory. The authors propose multiple conformal prediction procedures tailored to jointly exchangeable arrays, including full conformal, split conformal, row-column, and selective conformal approaches. For missing data elements, they establish asymptotic validity of a graphon-weighted conformal procedure under a nonparametric graphon model. Notably, the work provides the first formal proof of asymptotic conditional validity for weighted conformal prediction under missing-not-at-random assumptions, with applications demonstrated on both synthetic and real network data.
What's missing
The paper does not discuss computational complexity or scalability of the proposed procedures to very large networks, nor does it provide detailed guidance on practical hyperparameter selection for practitioners implementing these methods.
What different sources said
- arXiv stat.MLCenter
Conformal Prediction for Dyadic Regression Under Complex Missingness
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