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New conformal prediction framework tackles complex missing data

By PulseAugur Editorial · [2 sources] ·

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

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical advancements in conformal prediction.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Robert Lunde, Minjie Yang, Elizaveta Levina, Ji Zhu ·

    Conformal Prediction for Dyadic Regression Under Complex Missingness

    arXiv:2606.11136v1 Announce Type: cross Abstract: We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we establish super-uniformity of conformal prediction under distributional invariance co…

  2. arXiv stat.ML TIER_1 English(EN) · Ji Zhu ·

    Conformal Prediction for Dyadic Regression Under Complex Missingness

    We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we establish super-uniformity of conformal prediction under distributional invariance conditions weaker than exchangeability. A key result…

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