Integrative Analysis on Heterogeneous Datasets with High-Dimensional and Non-Standard Models

高维非标准模型异构数据集综合分析

• 批准号: 1916271
• 负责人: Yuekai Sun
• 金额: $ 18万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916271&HistoricalAwards=false
• 关键词: Integrative; Analysis; Heterogeneous; Datasets; Dimensional

Advances in data collection technology in the past decade have enabled practitioners to collect larger and more comprehensive datasets about many natural and social phenomena. Although this trend has enabled practitioners to gain new insights, it also comes with caveats that, if not addressed, may lead to erroneous conclusions that lie at the core of the reproducibility crisis in some areas of science. The caveats include: (i) Modern datasets are growing in heterogeneity, not only as a consequence of the inherent diversity in the world, but also the trend of combining data from multiple sources to create more comprehensive datasets. Not properly accounting for this growing heterogeneity may lead practitioners to systematically biased conclusions. (ii) The size of modern datasets is a hindrance to drawing inferences from them. Fitting a standard model to a massive dataset can be computationally intractable. (iii) The comprehensive nature of modern datasets raises privacy and security concerns. This is exacerbated by integrative analysis that may uncover combinations of patterns in multiple sources that are individually innocuous, but jointly identifying.The Principal Investigator aims to address the heterogeneity, size, and privacy/security concerns that arise in integrative analysis of heterogeneous datasets by designing communication avoiding methods. At a high level, the general approach is to trade local computation for communication: compute lossy summaries of each data source and perform integrative analysis on the summaries. This way, only the summaries are assembled, thereby reducing the communication costs and preserving the anonymity and security of the separate data sources. The specific aims of the PI's work include (i) effective computational strategies for distributed computing under heterogeneity in popular high-dimensional models with provable statistical guarantees, and (ii) new methodological and theoretical insights into data integration for "non-differentiable" statistical problems in which estimators are obtained by projecting either on the boundaries of a convex cone or via the optimization of discontinuous criterion functions, and which arise increasingly in modern domains of research.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.

过去十年数据收集技术的进步使从业人员能够收集关于许多自然和社会现象的更大和更全面的数据集。虽然这一趋势使从业者能够获得新的见解，但也有警告说，如果不加以解决，可能会导致错误的结论，这些结论是某些科学领域再现性危机的核心。警告包括：（i）现代数据集的异质性正在增加，这不仅是因为世界固有的多样性，而且是因为有将多个来源的数据结合起来创建更全面的数据集的趋势。如果不适当地解释这种日益增长的异质性，可能会导致从业者系统性地得出有偏见的结论。(ii)现代数据集的规模是从中得出推论的障碍。将标准模型拟合到海量数据集在计算上可能是难以处理的。(iii)现代数据集的综合性质引起了隐私和安全问题。这是加剧了综合分析，可能会发现在多个来源的模式，单独无害的组合，但共同identifiing.The首席研究员的目的是解决异构性，规模和隐私/安全问题，在异构数据集的综合分析中出现的设计通信避免方法。在高层次上，一般的方法是用本地计算来交换通信：计算每个数据源的有损摘要，并对摘要进行综合分析。通过这种方式，仅汇总摘要，从而降低了通信成本，并保持了单独数据源的匿名性和安全性。PI工作的具体目标包括：（i）在具有可证明统计保证的流行高维模型中，在异质性下进行分布式计算的有效计算策略，和（ii）新的方法和理论的见解，数据集成的“不可微”一种统计问题，其中的估计量是通过投影到凸锥的边界上或通过不连续准则的优化来获得的该奖项反映了NSF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。

项目成果

Circumventing superefficiency: An effective strategy for distributed computing in non-standard problems

规避超效率：非标准问题分布式计算的有效策略

• DOI: 10.1214/19-ejs1559
• 发表时间: 2019
• 期刊: Electronic Journal of Statistics
• 影响因子: 1.1
• 作者: Banerjee, Moulinath;Durot, Cécile
• 通讯作者: Durot, Cécile

Federated Learning with Matched Averaging

• 发表时间: 2020-02
• 期刊: ArXiv
• 作者: Hongyi Wang;M. Yurochkin;Yuekai Sun;Dimitris Papailiopoulos;Y. Khazaeni

DIVIDE AND CONQUER IN NONSTANDARD PROBLEMS AND THE SUPER-EFFICIENCY PHENOMENON

• DOI: 10.1214/17-aos1633
• 发表时间: 2019-04-01
• 期刊: ANNALS OF STATISTICS
• 影响因子: 4.5
• 作者: Banerjee, Moulinath;Durot, Cecile;Sen, Bodhisattva
• 通讯作者: Sen, Bodhisattva

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions

• 发表时间: 2019-12
• 期刊: J. Mach. Learn. Res.
• 作者: Subha Maity;Yuekai Sun;M. Banerjee

Dirichlet Simplex Nest and Geometric Inference

• 发表时间: 2019-05
• 作者: M. Yurochkin;Aritra Guha;Yuekai Sun;X. Nguyen