Estimation, inference and testing for large-scale directed network models

大规模有向网络模型的估计、推理和测试

• 批准号: 1811767
• 负责人: Garvesh Raskutti
• 金额: $ 15万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811767&HistoricalAwards=false
• 关键词: Estimation; inference; testing; large; scale

Large-scale interaction networks naturally arise in many modern scientific applications. For example, in biochemistry and systems biology, amino acids in different locations of the protein sequence interact, while in computational neuroscience, connectivity networks amongst neurons in the brain naturally trigger responses to particular stimuli. This project will develop reliable and scalable algorithms for learning the underlying interaction network amongst many nodes. Due to both the scale, complexity, and the changing data technologies in the applications described above, the solutions to the challenges addressed in this project will lead both to the development of novel theory and methodology, and the implementation of new algorithms for the application domains. The goal of the project is to address the challenge of estimation, inference and testing for large-scale network models. Given the size of the networks generated, this project presents a number of computational and statistical challenges the PI will address by focusing on two methodologies: (i) multivariate time series models; (ii) directed graphical models. The PI's prior work has developed new theory and methodology both for large-scale non-linear time series models and directed graphical models. This prior work points to a number of significant open challenges for both methodologies that this project will. These challenges include: (i) lack of sample size/statistical resources for learning complicated dependence structures; (ii) computational challenges due to non-convexity and large search-spaces for dependence models; (iii) incorporating domain knowledge and scientific experiments into the estimation methodologies; and (iv) exploiting learned networks for hypothesis testing, inference, and parameter estimation. This project will address these challenges and these contributions will lead to the development of new methods for network 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.

大规模交互网络自然出现在许多现代科学应用中。例如，在生物化学和系统生物学中，蛋白质序列不同位置的氨基酸相互作用，而在计算神经科学中，大脑中神经元之间的连接网络自然触发对特定刺激的反应。该项目将开发可靠和可扩展的算法，用于学习许多节点之间的底层交互网络。由于上述应用程序的规模、复杂性和不断变化的数据技术，本项目所解决的挑战的解决方案将导致新理论和方法的发展，以及应用程序领域的新算法的实现。该项目的目标是解决大规模网络模型的估计、推理和测试的挑战。鉴于所生成的网络的规模，该项目提出了许多计算和统计方面的挑战，PI将通过关注两种方法来解决：(i)多变量时间序列模型；（ii）有向图形模型。PI先前的工作为大规模非线性时间序列模型和有向图形模型开发了新的理论和方法。这项先前的工作指出了本项目将面临的两种方法的一些重大挑战。这些挑战包括：(i)缺乏用于学习复杂依赖结构的样本量/统计资源；（ii）依赖模型的非凸性和大搜索空间带来的计算挑战；（三）将领域知识和科学实验纳入估算方法；（iv）利用学习网络进行假设检验、推理和参数估计。这个项目将解决这些挑战，这些贡献将导致网络学习新方法的发展。该奖项反映了美国国家科学基金会的法定使命，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

Lazy Estimation of Variable Importance for Large Neural Networks

• DOI: 10.48550/arxiv.2207.09097
• 发表时间: 2022-07
• 作者: Yue Gao;Abby Stevens;Garvesh Raskutti;R. Willett

The bias of isotonic regression

• DOI: 10.1214/20-ejs1677
• 发表时间: 2020-01-01
• 期刊: ELECTRONIC JOURNAL OF STATISTICS
• 影响因子: 1.1
• 作者: Dai, Ran;Song, Hyebin;Raskutti, Garvesh
• 通讯作者: Raskutti, Garvesh

ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching

• DOI: 10.1137/19m126476x
• 发表时间: 2019-11
• 期刊: ArXiv
• 作者: Anru R. Zhang;Yuetian Luo;Garvesh Raskutti;M. Yuan

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes

• 发表时间: 2020
• 期刊: AI Mag.
• 作者: Hao Chen;Lili Zheng;R. Kontar;Garvesh Raskutti

PUlasso: High-Dimensional Variable Selection With Presence-Only Data

PUlasso：仅存在数据的高维变量选择

• DOI: 10.1080/01621459.2018.1546587
• 发表时间: 2018
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Song, Hyebin;Raskutti, Garvesh
• 通讯作者: Raskutti, Garvesh