CAREER: The optimal use of data

数据收集的现代技术？源自医学和生物信息学、互联网应用如网络搜索、物理和天文学、移动数据收集平台？产生了数据量和多样性的爆炸式增长。同时，统计学、决策理论和机器学习已经成功地为回答基于这些数据分析的关于我们世界的问题奠定了基础。随着收集到的信息越来越多，经典的推理和学习方法是不够的，因为出现了更多的问题。计算资源、隐私考虑、存储限制、网络通信约束？在统计准确性之外。这就提出了一个基本问题：如何在保持统计性能的同时平衡多个标准？为了将统计学和机器学习与其他需要的领域更紧密地联系起来，这项研究涉及到以原则和最佳方式在稀缺资源之间进行交易的程序的开发。这种权衡很难描述，因为目前提供基本限制的工具（如通信中的信息理论）不能连接不同的领域。三个具体的子领域作为本研究的基础。研究者通过连接计算的概念来研究计算与学习、估计和优化之间的相互作用。比如分布式系统中的内存访问或同步？到数据分析任务。其次，该研究调查了适应性和稳健的程序？相关的统计成本呢？鉴于数据越来越长尾和混乱，这将变得更加重要。第三，研究人员研究了估计中的隐私，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。结合起来，这些奠定了在面对约束时使用数据的理论基础，以及对平衡稀缺资源和统计准确性的程序的功能和实际理解。

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.