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New framework offers uncertainty quantification for graph-valued AI predictions

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework offers uncertainty quantification for graph-valued AI predictions

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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]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Gabriel Melo, Thibaut de Saivre, Anna Calissano, Florence d'Alch\'e-Buc ·

    Conformal Graph Prediction with Z-Gromov-Wasserstein Distances

    arXiv:2603.02460v5 Announce Type: replace Abstract: Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncertainty quantification remains limited. We propose …

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