用神经网络学习条件选择策略，加速概率图模型的最可能解释推理。
Learning to Condition: A Neural Heuristic for Scalable MPE Inference
- 训练神经网络预测变量取值对推理的条件价值，指导搜索方向。
- 在高树宽模型上显著缩小搜索空间，且解质量优于现有方法。
- 适合需要快速推理的复杂概率建模任务，如医疗诊断或智能决策。
我们提出学习到的条件（L2C），一种可扩展的数据驱动框架，用于加速概率图模型（PGMs）中的最可能解释（MPE）推理，该问题本质上是不可 tractable（难以处理）的。L2C通过训练神经网络，根据已观测证据对变量-值赋值进行评分，以评估其作为条件的效用。为支持监督学习，我们开发了一种可扩展的数据生成流水线，从现有MPE求解器的搜索轨迹中提取训练信号。训练后的网络作为启发式策略，可集成至搜索算法中：既可作为精确推理前的条件策略，也可作为分支定界求解器中的分支与节点选择策略。我们在涉及高树宽PGMs的挑战性MPE查询上评估了L2C。实验表明，所学启发式显著缩小搜索空间，同时在解质量上保持或超越当前最优方法。
原文摘要 · Abstract (English)
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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