权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Learning and Leveraging the Structure of Large Graphs: Novel Theory and Algorithms

职业：学习和利用大图的结构：新颖的理论和算法

基本信息

批准号：
2048223
负责人：
Gautam Dasarathy
金额：
$ 59.55万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048223&HistoricalAwards=false
关键词：
CAREER Learning Leveraging Structure Large

项目摘要

From genetic interaction networks and the brain to wireless sensor networks and the power grid, there exist many large, complex interacting systems. Graph theory provides an elegant and powerful mathematical formalism for quantifying and leveraging such interactions. Unsurprisingly, many modern tasks in science and engineering rely on the discovery and exploitation of the structure of graphs. Unfortunately, there is a stark disconnect between the purported capabilities of data-driven algorithms for graph analytics and their real world applicability. Specifically, the following key challenges emerge for existing algorithms: (i) Reliance on large number of expensive experiments/measurements; this is prohibitive in the large systems typically encountered in science and engineering. (ii) Reliance on the availability of curated and labeled datasets; this is untenable outside a narrow set of disciplines. (iii) Design for worst-case scenarios; this lack of adaptivity to structure unique to the problem severely impairs their statistical and computational efficiency. In response to the above challenges, this research program will close the loop on traditional machine learning systems where data acquisition and learning algorithms are designed separately. The project will devise several novel compressive, adaptive, and interactive algorithms that efficiently exploit structure in the problem. These will be complemented by foundational advances to the theory of learning and leveraging structure in graphs. The methodological advances will have impact on diverse areas such as resilient cyber-infrastructure, robust neuroimaging, and intervention design for pandemics. The research activities are tightly integrated with a comprehensive education, mentoring, and outreach plan that will increase awareness, access, and inclusion in STEM, especially with respect to data-driven methods in science and engineering. The technical contributions of this project are organized into two interrelated themes: (1) Learning the structure of graphs from compressively and interactively acquired data. The research in this theme will reveal new and interesting tradeoffs between the cost of data acquisition and statistical accuracy. These will be complemented by minimax optimal algorithms that achieve various points in the tradespace. (2) Leveraging the graph structure to accomplish efficient inference. The research in this theme is unified by the general problem of level set estimation on graphs and will result in foundational contributions to the theory of nonparametric learning, meta-learning, and sequential decision making. The research themes feature extensive experimental validation, collaboration with domain experts, and translational activities with the view of driving meaningful and long-term impacting on practice.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.

从遗传交互网络和大脑到无线传感器网络和电网，存在着许多大型、复杂的交互系统。图论为量化和利用这种相互作用提供了一种优雅而强大的数学形式。不出所料，科学和工程中的许多现代任务都依赖于对图形结构的发现和开发。不幸的是，数据驱动的图形分析算法声称的能力与它们在现实世界中的适用性之间存在着明显的脱节。具体地说，现有算法面临以下关键挑战：(I)依赖大量昂贵的实验/测量；这在科学和工程中通常遇到的大系统中是令人望而却步的。(2)依赖于提供经过整理和标记的数据集；如果没有一套狭窄的学科，这是站不住脚的。(3)针对最坏情况的设计；这种对该问题特有的结构缺乏适应性，严重损害了它们的统计和计算效率。为了应对上述挑战，该研究计划将关闭传统机器学习系统中数据采集和学习算法分开设计的循环。该项目将设计几种新的压缩、自适应和交互算法，有效地利用问题中的结构。这些将得到学习和利用图形结构理论的基础性进展的补充。方法学上的进步将对多个领域产生影响，例如具有弹性的网络基础设施、强大的神经成像以及针对流行病的干预设计。研究活动与全面的教育、指导和推广计划紧密结合在一起，该计划将提高STEM的认识、机会和包容性，特别是在科学和工程领域的数据驱动方法方面。该项目的技术贡献分为两个相互关联的主题：(1)从压缩和交互获得的数据中学习图形的结构。这一主题的研究将揭示数据获取成本和统计准确性之间新的有趣的权衡。这些将得到极小极大优化算法的补充，这些算法在Tradesace中实现了不同的点。(2)利用图结构实现高效推理。这一主题的研究统一于图上水平集估计的一般问题，将对非参数学习、元学习和顺序决策理论产生基础性的贡献。研究主题包括广泛的实验验证，与领域专家的合作，以及翻译活动，以期对实践产生有意义和长期的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise

DOI：
10.1109/tpwrs.2022.3204232
发表时间：
2022-08
期刊：
IEEE Transactions on Power Systems
影响因子：
6.6
作者：
A. Varghese;A. Pal;Gautam Dasarathy
通讯作者：
A. Varghese;A. Pal;Gautam Dasarathy

Class GP: Gaussian Process Modeling for Heterogeneous Functions

GP 类：异质函数的高斯过程建模

DOI：
发表时间：
2023
期刊：
LION 17
影响因子：
0
作者：
Malu, M.;Pedrielli, G.;Dasarathy, G.;Spanias, A.
通讯作者：
Spanias, A.

A Label-Efficient Two-Sample Test

标签高效的双样本测试

DOI：
发表时间：
2022
期刊：
Uncertainty in artificial intelligence
影响因子：
0
作者：
Li, Weizhi;Dasarathy, Gautam;Ramamurthy, Karthikeyan;Berisha, Visar
通讯作者：
Berisha, Visar

Bayesian Optimization in High-Dimensional Spaces: A Brief Survey

高维空间中的贝叶斯优化：简要概述

DOI：
发表时间：
2021
期刊：
International Conference on Information Intelligence Systems and Applications
影响因子：
0
作者：
Malu, Mohit;Dasarathy, Gautam;Spanias, Andreas
通讯作者：
Spanias, Andreas

A Graph-Based Approach to Boundary Estimation With Mobile Sensors

基于图形的移动传感器边界估计方法