CAREER: Fast, Accurate Estimation and Prediction using Markov Logic

马尔可夫逻辑网络（MLN）通常用于各种应用领域，包括信息提取，计算机视觉，生物信息学和自然语言理解，以表示和推理关系和概率知识。然而，推理和学习是极具挑战性的，尽管近年来取得了巨大进展，但现实世界中的几个关键推理任务仍然遥不可及。这个CAREER奖项的目标是大大提高MLN学习和推理算法的可扩展性和准确性，从而解决比今天更大更难的推理问题。该奖项还包括一个紧密结合的教育和推广部分。具体而言，它（1）让高中生和大学生，特别是年轻女性参与开发和建模练习，鼓励她们从事研究工作;（2）提供开放源码软件工具，以促进和扩大MLN技术的采用;以及（3）通过组织推理竞赛促进数据集和评估方法的标准化。研究的目的是解决提升概率推理算法的两个基本限制，提升概率推理算法是MLN中推理和学习的主要方法。首先，现有的提升方法主要利用精确对称性而忽略近似对称性。其次，具有大量对称性的MLN通常不足以准确地建模复杂的、真实世界的依赖性和隐藏的现象。CAREER奖通过开发以下内容来解决这些问题：（1）利用近似对称性的原则方法;（2）使用结构化潜在变量来诱导多样化，高度表达的MLN的新颖学习算法;及（3）一个统一的信息-传递框架称为提升的结构化消息传递，系统地利用近似对称和结构化表示来解决MLN上定义的一系列推理任务包括边际估计、最大后验估计和边际最大后验估计。

Markov logic networks (MLNs) are routinely used in a wide variety of application domains including information extraction, computer vision, bio-informatics, and natural language understanding to represent and reason about relational and probabilistic knowledge. However, inference and learning in them is extremely challenging and despite tremendous progress in recent years several key real-world reasoning tasks remain out of reach. The goal of this CAREER award is to vastly improve the scalability and accuracy of learning and inference algorithms for MLNs and thus solve much larger and harder reasoning problems than is possible today. The award also includes a tightly integrated education and outreach component. Specifically, it (1) involves high school students as well as undergraduate students, especially young women in development and model building exercises, encouraging them to pursue career in research; (2) yields open source software tools to facilitate and broaden the adoption of MLN technology; and (3) promotes standardization of datasets and evaluation methodologies via organization of inference competitions.The key technical contribution of the proposed research is to address two fundamental limitations of lifted probabilistic inference algorithms, the dominating approach for inference and learning in MLNs. First, existing lifted methods primarily exploit exact symmetries and ignore approximate symmetries. Second, MLNs having large number of symmetries are often not expressive enough to accurately model complex, real-world dependencies and hidden phenomena. The CAREER award addresses these issues by developing: (1) principled approaches that exploit approximate symmetries; (2) novel learning algorithms that use structured latent variables to induce diverse, highly expressive MLNs; and (3) a unifying message-passing framework called lifted structured message passing that systematically exploits approximate symmetries and structured representations for solving a range of inference tasks defined over MLNs including marginal estimation, maximum-a-posteriori estimation and marginal maximum-a-posteriori estimation.