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CDS&E: Collaborative Research: Scalable Nonparametric Learning for Massive Data with Statistical Guarantees

CDS

基本信息

批准号：
2005779
负责人：
Zuofeng Shang
金额：
$ 12.76万
依托单位：
New Jersey Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005779&HistoricalAwards=false
关键词：
CDS&amp Collaborative Research Scalable Nonparametric

项目摘要

We now live in the era of data deluge. The sheer volume of the data to be processed, together with the growing complexity of statistical models and the increasingly distributed nature of the data sources, creates new challenges to modern statistics theory. Standard machine learning methods are no longer able to accommodate the computational requirements. They need to be re-designed or adapted, which calls for a new generation of design and theory of scalable learning algorithms for massive data. This project aims to provide a collection of state-of-the-art nonparametric learning tools for big data analysis, which can be directly used by scientists and practitioners and have beneficial impacts on various fields such as biomedicine, health-care, defense and security, and information technology. The deliverables of this project include easy-to-use software packages that will be thoroughly evaluated using a range of application examples. They will directly help scientists to explore and analyze complex data sets. Due to storage and computational bottlenecks, traditional statistical inferential procedures originally designed for a single machine are no longer applicable to modern large datasets. This project aims to design new scalable learning algorithms of wide-ranging nonparametric models for data that are distributed across a large number of multi-core computational nodes, or in a fashion of random sketching if only a single machine is available. The computational limits of these new algorithms will be examined from a statistical perspective. For example, in the divide-and-conquer setup, the number of deployed machines can be viewed as a simple proxy for computing cost. The project aims to establish a sharp upper bound for this number: when the number is below this bound, statistical optimality (in terms of nonparametric estimation or testing) is achievable; otherwise, statistical optimality becomes impossible. Related questions will also be addressed in the randomized sketching method in terms of the minimal number of random projections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

我们现在生活在数据泛滥的时代。要处理的数据的绝对数量，加上统计模型的日益复杂和数据源的日益分散的性质，对现代统计理论提出了新的挑战。标准的机器学习方法不再能够适应计算需求。它们需要重新设计或调整，这需要新一代的大规模数据可扩展学习算法的设计和理论。该项目旨在为大数据分析提供一系列最先进的非参数学习工具，这些工具可以直接被科学家和从业者使用，并对生物医学、卫生保健、国防和安全以及信息技术等各个领域产生有益的影响。该项目的可交付成果包括易于使用的软件包，这些软件包将使用一系列应用程序示例进行彻底评估。它们将直接帮助科学家探索和分析复杂的数据集。由于存储和计算瓶颈，最初为单个机器设计的传统统计推理程序不再适用于现代大型数据集。该项目旨在为分布在大量多核计算节点上的数据设计新的可扩展的非参数模型学习算法，或者如果只有一台机器可用，则以随机草图的方式。这些新算法的计算限制将从统计的角度进行检查。例如，在分而治之的设置中，可以将部署的机器数量视为计算成本的简单代理。该项目旨在为该数字建立一个明确的上界：当该数字低于该上界时，可以实现统计最优性（在非参数估计或检验方面）；否则，统计最优性就不可能实现。在随机写生法中，也会根据随机投影的最小数量来解决相关问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。