Learning to Condition: A Neural Heuristic for Scalable MPE Inference

• URL: http://arxiv.org/abs/2509.25217v1
• Date: Mon, 22 Sep 2025 18:24:31 GMT
• Title: Learning to Condition: A Neural Heuristic for Scalable MPE Inference
• Authors: Brij Malhotra, Shivvrat Arya, Tahrima Rahman, Vibhav Giridhar Gogate,
• Abstract summary: We introduce learning to condition (L2C), a scalable, data-driven framework for accelerating Most Probable Explanation (MPE) inference.<n>L2C trains a neural network to score variable-value assignments based on their utility for conditioning, given observed evidence.<n>We develop a scalable data generation pipeline that extracts training signals from the search traces of existing MPE solvers.
• Score: 7.287294240824019
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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