CAREER: A New Neat Framework for Statistical Machine Learning

职业：统计机器学习的新简洁框架

• 批准号: 1661755
• 负责人: Pradeep Ravikumar
• 金额: $ 22.94万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661755&HistoricalAwards=false
• 关键词: CAREER; New; Neat; Framework; Statistical

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.

人工智能（AI）研究的钟摆周期性地从所谓的“整洁”或数学严谨的方法，到“邋遢”或更临时的方法之间摇摆。近年来，科学和工程各个领域的真实数据变得越来越复杂，并且涉及大量变量，这导致了更复杂的方法激增。本提案通过利用机器学习方法的两个最流行的子领域：图形模型和高维统计方法的最新发展，为这种现代设置开发了一个通用的“整洁”框架。这些发展的共同点是，将复杂的模型参数表示为简单组件的叠加，然后利用它进行易于处理的推理和学习。我们的统一框架不仅形成了这些开发的统一图景，而且还提供了处理这些高维数据的新方法。因此，该研究影响了使用统计机器学习方法的科学和工程领域的问题（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“对比”思维训练的努力高度相关。这结合了解决现代统计人工智能应用领域固有的有限计算和有限数据问题所需的计算和统计思维。该提案还为基于组件的课程开发了一个基础设施，该基础设施具有相互组织的讲座模块组件。