CAREER: The optimal use of data

职业：数据的最佳利用

• 批准号: 1553086
• 负责人: John Duchi
• 金额: $ 49.7万
• 依托单位: Stanford 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: 2016-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553086&HistoricalAwards=false
• 关键词: CAREER; optimal; use; data

Modern techniques for data gathering?arising from medicine and bioinformatics, internet applications such as web-search, physics and astronomy, mobile data gathering platforms?have yielded an explosion in the mass and diversity of data. Concurrently, statistics, decision theory, and machine learning have successfully laid a groundwork for answering questions about our world based on analysis of this data. As more information is collected, classical approaches for inference and learning are insufficient, as additional concerns arise?computational resources, privacy considerations, storage limitations, network communication constraints? outside of statistical accuracy. This prompts a basic question: how can multiple criteria be balanced while maintaining statistical performance?To bring statistics and machine learning into closer contact with other desiderata, this research involves the development of procedures that trade between scarce resources in principled and optimal ways. Such trade-offs have been difficult to characterize, as current tools for providing fundamental limits (such as information theory in communication) do not connect disparate areas. Three concrete sub-areas serve as bases for this research. The investigators study the interplay of computing with learning, estimation, and optimization by connecting notions of computation?such as memory accesses or synchronization in distributed systems?to data analysis tasks. Second, the research investigates adaptive and robust procedures?and associated statistical costs?that will become more important given increasingly long-tailed and messy data. Thirdly, the investigators study privacy in estimation, using information and decision-theoretic tools to characterize the tensions between statistical accuracy and sensitive data disclosures. Combined, these lay the groundwork for a theory on the use of data in the face of constraints, along with a functional and practical understanding of procedures that balance scarce resources against statistical accuracy.

现代数据收集技术--源于医学和生物信息学、网络搜索、物理和天文学等互联网应用、移动数据收集平台--导致了数据的海量和多样性的爆炸性增长。同时，统计学、决策理论和机器学习已经成功地为基于对这些数据的分析回答关于我们世界的问题奠定了基础。随着收集的信息越来越多，经典的推理和学习方法已经不够充分，因为出现了额外的顾虑：计算资源、隐私考虑、存储限制、网络通信限制？超出了统计的准确性。这引发了一个基本问题：如何在保持统计性能的同时平衡多个标准？为了使统计学和机器学习与其他需求数据更紧密地联系在一起，这项研究涉及到开发程序，以原则性和最佳方式在稀缺资源之间进行交易。这种权衡很难描述，因为目前提供基本限制的工具(如通信中的信息论)不能将不同的领域联系起来。三个具体的分区是本研究的基础。研究人员通过将计算概念(如分布式系统中的内存访问或同步)与数据分析任务联系起来，研究计算与学习、评估和优化之间的相互作用。其次，这项研究调查了适应性和健壮的程序？以及相关的统计成本？鉴于数据越来越长尾和凌乱，这一点将变得更加重要。第三，研究人员研究估计中的隐私，使用信息和决策理论工具来表征统计准确性和敏感数据披露之间的紧张关系。这些因素结合在一起，为在面临限制的情况下使用数据的理论奠定了基础，并对平衡稀缺资源与统计准确性的程序有了实用的理解。

项目成果

The importance of better models in stochastic optimization

• DOI: 10.1073/pnas.1908018116
• 发表时间: 2019-11-12
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Asi, Hilal;Duchi, John C.
• 通讯作者: Duchi, John C.