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）研究的摆段定期从所谓的“整洁”或数学上严格的方法和“ scruffy”或更多的Adhoc方法中转动。近年来，跨科学和工程领域的现实世界数据越来越复杂，并且涉及大量变量，这导致了大量的易碎方法。该提案通过利用最流行的机器学习方法的两个子场中的最流行的子领域来为这种现代环境开发一个通用的“整洁”框架：图形模型和高维统计方法。这些发展共同认为，复杂的模型参数表示为简单组件的叠加，然后将其用于可拖延的推理和学习。我们的统一框架结果不仅在这些发展的统一图片中，而且还提供了与此类高维数据一起使用的更新方法。因此，该研究会影响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，仅举几例）。在统一机器学习问题的统一框架上的工作高度结合，推动向学生提供所谓的“ comptastical”思维的培训。这结合了解决有限计算问题和现代统计AI应用程序域固有的有限数据所需的计算和统计思维。该提案还开发了一个基于组件的课程的基础架构，该课程具有关系组织的讲座模块组件。