CAREER: A New Neat Framework for Statistical Machine Learning

人工智能（AI）研究的钟摆周期性地从所谓的“整洁”或数学上严格的方法，和“肮脏”或更特设的方法。近年来，科学和工程各个领域的真实世界数据越来越复杂，涉及大量变量，这导致了大量的粗糙方法。该提案通过利用机器学习方法中两个最流行的子领域的最新发展，为这种现代环境开发了一个通用的“整洁”框架：图形模型和高维统计方法。这些发展有一个共同点，即一个复杂的模型参数被表示为简单组件的叠加，然后利用易于处理的推理和learning.Our统一的框架结果不仅在这些发展的统一图片，但也提供了更新的方法来处理这样的高维数据。因此，该研究影响了使用统计机器学习方法的科学和工程问题（例如基因组学，自然语言处理和图像分析等）。为统计机器学习问题建立统一框架的工作，与向学生传授我们所谓的“计算”思维的培训密切相关。这结合了计算和统计思维，解决了现代统计人工智能应用领域固有的有限计算和有限数据的问题。该提案还开发了一个基础设施，以组件为基础的课程与关系组织讲座模块组件。

The pendulum in Artificial Intelligence (AI) research has periodically swung from so called "neat" or mathematically rigorous approaches, and "scruffy" or more adhoc approaches. In recent years, real-world data across varied fields of science and engineering are increasingly complex, and involve a large number of variables, which has resulted in a surge of scruffier methods. This proposal develops a general "neat" framework for such modern settings by leveraging state of the art developments in two of the most popular subfields of machine learning methods: graphical models and high-dimensional statistical methods. These developments have in common that a complex model parameter is expressed as a superposition of simple components, which is then leveraged for tractable inference and learning.Our unified framework results not only in a unified picture of these developments but also provides newer methods to work with such high-dimensional data. The research thus impacts problems across science and engineering wherever statistical machine learning approaches are being used (such as genomics, natural language processing and image analysis, to name a few). The work on a unified framework for statistical machine learning problems is highly coupled with a push for imparting training to students on what we call "comptastical" thinking. This combines both computational and statistical thinking required for addressing the problems of limited computation and limited data inherent in modern statistical AI application domains. The proposal also develops an infrastructure for component-based courses with relationally organized lecture module components.