CCSS: Collaborative Research: Sketching for High Dimensional Data Analysis in IoT

CCSS：协作研究：物联网高维数据分析草图

• 批准号: 2000415
• 负责人: Lifeng Lai
• 金额: $ 20万
• 依托单位: University of California-Davis
• 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: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000415&HistoricalAwards=false
• 关键词: CCSS; Collaborative; Research; Sketching; Dimensional

The proliferation of IoT devices generates enormous amount of data, which leads to a tremendous demand on resources for the transmission, processing and storage. In order to extract useful information and fully achieve the potential of IoT for intelligent decisions, it is critical to develop novel systems to efficiently transmit, store and process this large volume of data. Fortunately, the data generated by IoT and many other applications typically possesses certain low dimensional parsimonious structures. Leveraging on these low dimensional structures of IoT data, this project will develop methods based on low dimensional sketches to infer information of interest. This sketching based framework will enable quick and accurate information extraction with greatly reduced sampling rates, transmission and storage costs.This project will develop a sketching based framework for the efficient processing of high dimensional data generated by IoT and investigate its theoretical and algorithmic properties. The core idea is to take low dimensional projections or sketches of random variables, rather than to directly and fully observe high dimensional random variables. This project will develop both non-adaptive and adaptive low dimensional sketching methods, and will make the following intellectual contributions: 1) deriving fundamental limits on sampling rates for recovering statistical information of high dimensional random variables; 2) designing explicit sampling schemes that achieve the corresponding fundamental limits on sampling rates; 3) providing fast algorithms with performance guarantees for recovering statistical information from sketches of random variables. The PIs will leverage their research experience in compressed sensing, low-rank matrix recovery and sequential analysis in solving these challenging problems. The generated results are expected to extend compressed sensing from sketching of deterministic values to a broader context of sketching of random variables.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.

物联网设备的激增产生了大量数据，这对传输、处理和存储资源提出了巨大的需求。为了提取有用信息并充分发挥物联网智能决策的潜力，开发新颖的系统来有效传输、存储和处理如此大量的数据至关重要。幸运的是，物联网和许多其他应用生成的数据通常具有某些低维简约结构。利用物联网数据的这些低维结构，该项目将开发基于低维草图的方法来推断感兴趣的信息。这种基于草图的框架将能够快速准确地提取信息，同时大大降低采样率、传输和存储成本。该项目将开发一种基于草图的框架，用于有效处理物联网生成的高维数据，并研究其理论和算法特性。其核心思想是对随机变量进行低维投影或草图，而不是直接、全面地观察高维随机变量。该项目将开发非自适应和自适应低维草图方法，并将做出以下智力贡献：1）导出用于恢复高维随机变量统计信息的采样率的基本限制； 2）设计明确的采样方案，以实现采样率的相应基本限制； 3) 提供具有性能保证的快速算法，用于从随机变量草图恢复统计信息。 PI 将利用他们在压缩感知、低秩矩阵恢复和序列分析方面的研究经验来解决这些具有挑战性的问题。生成的结果预计将压缩感知从确定性值的草图扩展到更广泛的随机变量的草图。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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

Privacy-Accuracy Trade-Off of Inference as Service

推理即服务的隐私与准确性权衡

• DOI: 10.1109/icassp39728.2021.9413438
• 发表时间: 2021-06
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Jin, Yulu;Lai, Lifeng
• 通讯作者: Lai, Lifeng

Projection Robust Wasserstein Barycenters

投影鲁棒 Wasserstein 重心

• 发表时间: 2021-01
• 期刊: Proceedings of Machine Learning Research
• 作者: Minhui Huang; Shiqian Ma
• 通讯作者: Shiqian Ma

On the Adversarial Robustness of Hypothesis Testing

关于假设检验的对抗稳健性

• DOI: 10.1109/tsp.2020.3045206
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Jin, Yulu;Lai, Lifeng
• 通讯作者: Lai, Lifeng

Distributed Dual Coordinate Ascent in General Tree Networks and Communication Network Effect on Synchronous Machine Learning

通用树网络中的分布式双坐标上升和通信网络对同步机器学习的影响

• DOI: 10.1109/jsac.2021.3078495
• 发表时间: 2021-07
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: Cho, Myung;Lai, Lifeng;Xu, Weiyu
• 通讯作者: Xu, Weiyu

Adversarially Robust Fairness-Aware Regression

对抗性鲁棒公平意识回归

• DOI: 10.1109/icassp49357.2023.10094637
• 发表时间: 2023-06
• 期刊: IEEE
• 作者: Jin, Yulu;Lai, Lifeng
• 通讯作者: Lai, Lifeng