在概率图模型中学习引导局部搜索用于MPE推断
Learning to Guide Local Search for MPE Inference in Probabilistic Graphical Models
AI总结：
本文提出神经元消融框架，通过学习引导局部搜索以提升概率图模型中MPE推断的效率与性能。
摘要：
在概率图模型（PGMs）中，最可能的解释（MPE）推断是一个基础但计算上具有挑战性的问题，出现在诊断、规划和结构预测等领域。在许多实际情况下，图形模型保持固定，而推断必须为变化的证据模式重复进行。随机局部搜索（SLS）算法能够扩展到大规模模型，但依赖于短视的最佳改进规则，优先考虑即时似然增益，往往停滞在较差的局部最优解。启发式方法如引导局部搜索（GLS+）部分缓解了这一限制，通过修改搜索景观，但其指导无法有效重用于同一模型上的多个推断查询。我们提出了一种神经元消融框架，以改进这种重复查询环境中的局部搜索。利用固定的图结构，我们训练了一个基于注意力的网络，通过预测其减少汉明距离到近最优解的能力来评分局部移动。我们的方法无缝集成到现有的局部搜索过程中，利用这一信号在邻居选择时平衡短期似然增益与长期前景。我们提供了将减少距离的移动选择与改进的收敛行为联系起来的理论直觉，并在消融推断设置中对具有挑战性的高树宽基准进行了实证演示，证明了与SLS和GLS+的一致性改进。
英文摘要：
Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, planning, and structured prediction. In many practical settings, the graphical model remains fixed while inference must be performed repeatedly for varying evidence patterns. Stochastic Local Search (SLS) algorithms scale to large models but rely on myopic best-improvement rule that prioritizes immediate likelihood gains and often stagnate in poor local optima. Heuristics such as Guided Local Search (GLS+) partially alleviate this limitation by modifying the search landscape, but their guidance cannot be reused effectively across multiple inference queries on the same model. We propose a neural amortization framework for improving local search in this repeated-query regime. Exploiting the fixed graph structure, we train an attention-based network to score local moves by predicting their ability to reduce Hamming distance to a near-optimal solution. Our approach integrates seamlessly with existing local search procedures, using this signal to balance short-term likelihood gains with long-term promise during neighbor selection. We provide theoretical intuition linking distance-reducing move selection to improved convergence behavior, and empirically demonstrate consistent improvements over SLS and GLS+ on challenging high-treewidth benchmarks in the amortized inference setting.
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