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arXiv · 2509.25217

Learning to Condition: A Neural Heuristic for Scalable MPE Inference

Brij Malhotra·Shivvrat Arya·Tahrima Rahman·Vibhav Giridhar Gogate
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Abstract

We introduce learning to condition (L2C), a scalable, data-driven framework for accelerating Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs), a fundamentally intractable problem. L2C trains a neural network to score variable-value assignments based on their utility for conditioning, given observed evidence. To facilitate supervised learning, we develop a scalable data generation pipeline that extracts training signals from the search traces of existing MPE solvers. The trained network serves as a heuristic that integrates with search algorithms, acting as a conditioning strategy prior to exact inference or as a branching and node selection policy within branch-and-bound solvers. We evaluate L2C on challenging MPE queries involving high-treewidth PGMs. Experiments show that our learned heuristic significantly reduces the search space while maintaining or improving solution quality over state-of-the-art methods.

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BibTeXRIS

Brij Malhotra, Shivvrat Arya, Tahrima Rahman, Vibhav Giridhar Gogate. 2025-09-22. Learning to Condition: A Neural Heuristic for Scalable MPE Inference. https://arxiv.org/abs/2509.25217

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