BIGDATA: Collaborative Research: F: Nomadic Algorithms for Machine Learning in the Cloud

BIGDATA：协作研究：F：云中机器学习的游牧算法

• 批准号: 1546452
• 负责人: Inderjit Dhillon
• 金额: $ 61.04万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546452&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Nomadic; Algorithms

With an ever increasing ability to collect and archive data, massive data sets are becoming increasingly common. These data sets are often too big to fit into the main memory of a single computer, and so there is a great need for developing scalable and sophisticated machine learning methods for their analysis. In particular, one has to devise strategies to distribute the computation across multiple machines. However, stochastic optimization and inference algorithms that are so effective for large-scale machine learning appear to be inherently sequential.The main research goal of this project is to develop a novel "nomadic" framework that overcomes this barrier. This will be done by showing that many modern machine learning problems have a certain "double separability" property. The aim is to exploit this property to develop convergent, asynchronous, distributed, and fault tolerant algorithms that are well-suited for achieving high performance on commodity hardware that is prevalent on today's cloud computing platforms. In particular, over a four year period, the following will be developed: (i) parallel stochastic optimization algorithms for the multi-machine cloud computing setting, (ii) theoretical guarantees of convergence, (iii) open source code under a permissive license, (iv) application of these techniques to a variety of problem domains such as topic models and mixture models. In addition, a cohort of students who can transfer their skills to both industry and academia will be trained, and a graduate level course on scalable machine learning will be developed. The proposed research will enable practitioners in different application areas to quickly solve their big data problems. The results of the project will be disseminated widely through papers and open source software. Course material will be developed for the education of students in the area of Scalable Machine Learning, and the course will be co-taught at UCSC and UT Austin. The project will recruit women and minority students.

随着收集和归档数据的能力不断增强，海量数据集变得越来越普遍。这些数据集通常太大，无法放入单个计算机的主存储器中，因此非常需要开发可扩展和复杂的机器学习方法来分析它们。特别是，必须设计策略将计算分布到多台机器上。然而，对于大规模机器学习如此有效的随机优化和推理算法似乎本质上是顺序的。该项目的主要研究目标是开发一种新的“游牧”框架来克服这一障碍。这将通过展示许多现代机器学习问题具有一定的“双重可分性”属性来实现。目的是利用这一特性来开发聚合、异步、分布式和容错算法，这些算法非常适合在当今云计算平台上流行的商用硬件上实现高性能。特别是，在四年期间，将开发以下内容：(i)用于多机器云计算设置的并行随机优化算法，（ii）收敛的理论保证，（iii）在许可许可下的开源代码，（iv）将这些技术应用于各种问题领域，如主题模型和混合模型。此外，将培训一批能够将其技能转移到工业界和学术界的学生，并将开发一门可扩展机器学习的研究生课程。拟议的研究将使不同应用领域的从业者能够快速解决他们的大数据问题。该项目的成果将通过论文和开源软件广泛传播。课程材料将用于可扩展机器学习领域的学生教育，课程将在加州大学圣迭戈分校和德克萨斯大学奥斯汀分校共同教授。该项目将招收女性和少数民族学生。