CIF: Small: Resource-Efficient Statistical Inference in Networked Environments

CIF：小型：网络环境中资源高效的统计推断

基本信息

批准号：
2007911
负责人：
Yuejie Chi
金额：
$ 49.77万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007911&HistoricalAwards=false
关键词：
CIF Small Resource Efficient Statistical

项目摘要

Recent years have witnessed tremendous growth in the need of distributed information processing, where the data are collected, stored, and processed in and through a large network of distributed agents. Examples include training better machine intelligence using mobile devices to improve user experiences, monitoring environments using a network of sensors to reduce and mitigate wildfire, improving the coordination of unmanned aerial vehicles in surveillance. This project embarks on a new framework to address emerging challenges in processing the ever-growing large-scale datasets in a resource-efficient manner that have been unaddressed until now. The investigators will actively recruit and train students with diverse backgrounds including underrepresented minorities and women in STEM through long-term mentoring and outreach activities.This project will substantially advance the algorithmic practice of statistical learning and inference from high-dimensional distributed data, by developing resource-efficient distributed statistical inference algorithms in network environments that provably achieve the optimal trade-offs in statistical error, computation and communication costs, thereby enabling scalable processing of decentralized and heterogenous data. Calling for a tight integration of high-dimensional statistical inference and large-scale decentralized optimization, this project consists of three major thrusts: (i) develop resource-efficient decentralized statistical estimation algorithms with provable convergence guarantees; (ii) develop systematic treatments to ensure algorithmic convergence even in the presence of highly unbalanced and heterogeneous data, as well as promote diversity for heterogeneous agents using graph regularization; (iii) promote optimal and adaptive early stopping criteria for decentralized nonparametric estimation. The tools and techniques developed herein will further foster the interplay between a broad range of fields including high-dimensional statistics, large-scale optimization, statistical signal processing, and machine learning.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.

近年来，在分布式信息处理的需求中，在收集，存储和通过大型分布式代理网络中收集，存储和处理数据的需要增长了巨大的增长。例如，使用移动设备来改善用户体验，使用传感器网络来减少和减轻野火，改善无人机监视中的无人机协调，从而改善了用户体验，以改善用户体验，从而改善环境。该项目着手开发一个新的框架，以应对不断增长的方式处理不断增长的大规模数据集，以解决新出现的挑战，这些数据集迄今尚未解决。调查人员将通过长期指导和外展活动在STEM中积极招募和培训具有多种背景的学生，包括占代表性不足的少数群体和妇女。该项目将基本上推进统计学习的算法实践，并从高维分布式数据中推断出高维分布式数据，从高维分布式数据中，从而通过开发效率的分布式统计算法来实现型号，从而实现了在网络上的启用，从而实现了在网络上的启用，从而实现了统计量的启用，从而实现了统计的统计算法。分散和异源数据的可扩展处理。要求紧密整合高维统计推断和大规模分散优化的优化，该项目由三个主要推力组成：（i）开发具有可证明的收敛保证的资源有效分散统计估计算法；（ii）开发系统处理，以确保算法收敛，即使存在高度不平衡和异质数据，并使用图形正则化促进异质剂的多样性；（iii）促进最佳和适应性的早期停止标准，用于分散的非参数估计。本文开发的工具和技术将进一步促进广泛领域之间的相互作用，包括高维统计，大规模优化，统计信号处理和机器学习。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智力功能和更广泛影响的评估来通过评估来获得支持的。