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Fast and Consistent Structure Learning in Graphical Models via Approximate Cross-Validation

NeurIPS 2026 · Graph learning › Graph algorithms and clustering · approach: Continuous DAG structure learning

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes approximate cross-validation for sparse graphical model structure learning to obtain fast, selection-consistent graph estimates without model-specific likelihood calibration.

Problem

Structure learning for sparse graphical models in high dimensions needs both computational scalability and selection consistency; classical criteria (E)BIC need model-specific calibration, while standard cross-validation is expensive.

Approach

Uses approximate cross-validation to select regularization for graph learning, avoiding repeated refitting while retaining the calibration-free nature of cross-validation and aiming for selection consistency. Abstract is truncated, so details beyond this are not stated.

Experimental setup

Baselines: Bayesian Information Criterion Extended Bayesian Information Criterion cross-validation

Builds on: cross-validation BIC extended BIC

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