In this paper, we examine the problem of partial inference in the context of structured prediction. Using a generative model approach, we consider the task of maximizing a score function with unary and pairwise potentials in the space of labels on graphs. Employing a two-stage convex optimization algorithm for label recovery, we analyze the conditions under which a majority of the labels can be recovered. We introduce a novel perspective on the Karush-Kuhn-Tucker (KKT) conditions and primal and dual construction, and provide statistical and topological requirements for partial recovery with provable guarantees.

翻译：本文研究了结构预测框架下的部分推断问题。采用生成式模型方法，我们考虑在图标签空间中最大化包含一元势函数与二元势函数的得分函数任务。通过运用两阶段凸优化算法进行标签恢复，我们分析了多数标签可被恢复的条件。本文提出了关于Karush-Kuhn-Tucker (KKT)条件以及原始-对偶构造的新视角，并给出了具有可证明保证的部分恢复所需的统计与拓扑条件。