New framework offers uncertainty quantification for graph-valued AI predictions

Researchers have developed a new conformal prediction framework designed for graph-valued outputs, offering distribution-free coverage guarantees in complex structured output spaces. This method utilizes the Z-Gromov-Wasserstein distance, specifically implemented via Fused Gromov-Wasserstein (FGW), to compare predicted and candidate graphs in a permutation-invariant manner. To achieve adaptive prediction sets, the approach extends Conformalized Quantile Regression (CQR) to a new method called Score Conformalized Quantile Regression (SCQR), which is suitable for graph-valued outputs. The effectiveness of this framework has been demonstrated on both synthetic data and a real-world application involving molecule identification. AI

IMPACT This research could improve the reliability of AI models that predict complex structured outputs like molecular graphs.

RANK_REASON The cluster contains a research paper detailing a new method for graph prediction with uncertainty quantification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Conformalized Quantile Regression
• Fused Gromov-Wasserstein
• Gabriel A.R. Melo
• Score Conformalized Quantile Regression
• Z-Gromov-Wasserstein

• paper
• model release

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