CAREER: Advances in Multi-scale Bayesian Inference and Learning on Massive Data

职业：多尺度贝叶斯推理和海量数据学习的进展

基本信息

批准号：
1749789
负责人：
Li Ma
金额：
$ 40万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749789&HistoricalAwards=false
关键词：
CAREER Advances Multi scale Bayesian

项目摘要

Massive data present unprecedented opportunities for advancing our understanding of various scientific and social phenomena. With sufficient data and the appropriate statistical tools, researchers can now hope to recover structures in the data that were once deemed too intricate to identify with traditional "small" data. Extracting complex hidden structures in massive data often requires flexible nonparametric methods; however, there are several fundamental challenges that make existing nonparametric methods impractical or inadequate. At the core of these challenges is a conflict between two essential aspects in big data analysis: (i) the need for flexible methodology for capturing complex features and (ii) the cost, both computational and statistical, associated with this additional flexibility. Effective resolution of this fundamental conflict requires new paradigms of nonparametric inference. The long-term research objective of this project is to develop inference paradigms, including theory, methods, algorithms, and software, for nonparametric inference and learning that effectively resolve this fundamental conflict. The research will lead to the development of statistical tools that meet urgent needs for scalable nonparametric data analysis in a wide range of fields, including biology, economics, astrophysics, chemistry, and information technology. The project will address the integration of research with educational activities through teaching and mentoring of undergraduate and graduate students, and outreach to students from local colleges.This project will develop and investigate a particularly promising paradigm, multi-scale divide-and-conquer, to address the fundamental conflict between flexibility and cost. Specific inference problems to be addressed cover a wide range of nonparametric inference and learning objectives, and can be organized into three research thrusts: (i) joint nonparametric modeling of multiple data generative processes; (ii) characterizing dependency between random variables/vectors; and (iii) response-domain ensemble supervised learning. Beyond addressing these specific objectives, the proposed research will introduce theoretical and computational devices for evaluating and improving the statistical and computational efficiency of multi-scale divide-and-conquer methods in general. The output of the research will include practical methods and algorithms for carrying out a variety of important nonparametric inference tasks on massive data, as well as general guiding principles for effective multi-scale statistical analysis. The research output will be disseminated through publications, presentations, and open-source software to the scientific community, and society at large.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.

大量数据提供了前所未有的机会，可以促进我们对各种科学和社会现象的理解。有了足够的数据和适当的统计工具，研究人员现在可以希望在曾经被认为过于复杂而无法识别传统“小”数据的数据中恢复结构。在大量数据中提取复杂的隐藏结构通常需要灵活的非参数方法。但是，存在一些基本挑战，使现有的非参数方法不切实际或不足。这些挑战的核心是大数据分析中的两个基本方面之间的冲突：（i）需要灵活的方法来捕获复杂的特征，以及（ii）计算和统计的成本，与这种额外的灵活性有关。有效解决这一基本冲突需要非参数推断的新范式。该项目的长期研究目标是开发推理范例，包括理论，方法，算法和软件，用于非参数推断和学习，从而有效地解决了这一基本冲突。这项研究将导致开发统计工具，这些工具满足迫切需求，以在包括生物学，经济学，天体物理学，化学和信息技术在内的广泛领域中可扩展的非参数数据分析。该项目将通过教学和指导本科生和研究生的教学和指导来解决研究与教育活动的融合，并向当地大学的学生推广。该项目将开发和调查特别有希望的范式，多尺度的分歧和互动，以解决灵活性和成本之间的基本冲突。要解决的特定推理问题涵盖了广泛的非参数推断和学习目标，并且可以组织为三个研究推力：（i）多个数据生成过程的联合非参数建模；（ii）表征随机变量/向量之间的依赖性；（iii）反应域合奏监督学习。除了解决这些特定目标之外，拟议的研究还将引入理论和计算设备，以评估和提高多规模分割和混合方法的统计和计算效率。该研究的输出将包括实用方法和算法，用于在大规模数据上执行各种重要的非参数推理任务，以及用于有效多规模统计分析的一般指导原则。研究成果将通过出版物，演讲和开源软件传播给科学界，以及整个社会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来支持的。