III: Small: Collaborative Research: Stream-Based Active Mining at Scale: Non-Linear Non-Submodular Maximization

III：小型：协作研究：基于流的大规模主动挖掘：非线性非子模最大化

• 批准号: 1908594
• 负责人: My Thai
• 金额: $ 25万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908594&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Stream

The past decades have witnessed enormous transformations of intelligent data analysis in the realm of datasets at an unprecedented scale. Analysis of big data is computationally demanding, resource hungry, and much more complex. With recent emerging applications, most of the studied objective functions have been shown to be non-submodular or non-linear. Additionally, with the presence of dynamics in billion-scale datasets, such as items are arriving in an online fashion, scalable and stream-based adaptive algorithms which can quickly update solutions instead of recalculating from scratch must be investigated. All of the aforementioned issues call for a scalable and stream-based active mining techniques to cope with enormous applications of non-submodular maximization in the era of big data. With the society's growing dependence on the cyberspace and computer technologies, the premium placed on the intelligent big data analysis for many emerging applications. Therefore, the success of this project has a high impact in almost any field that needs lightweight and near-optimal big data analysis. The findings of this project will also enrich the research on network science, graph theory, optimization, and big data analysis. In addition to creating new courses, undergrad and high school students will be involved in hands-on activities over the experimental platform. Outreach events targeted at under-represented groups and K-1This project develops a theoretical framework together with highly scalable approximation algorithms and tight theoretical performance bound guarantees for the class of non-submodular and non-linear optimization. In particular, the project lays the foundation for the novel data mining techniques, suitable to the new era of big data with emerging applications, as well as advance the research front of stochastic and stream-based algorithm designs, with several key innovations: 1) Rigorous mathematical techniques to analyze and design highly scalable approximation algorithms to the class of non-monotonic, non-submodular maximization, which underlies many emerging applications. 2) Attempt a new research direction by bridging the non-linear optimization and the combinatorial optimization, thereby bringing the new angles for the study of non-submodular optimization as well as getting deeper understanding of the problem structures. 3) Novel stream-based active mining at scale for multiple applications, focused on the two general models which unify many optimization problems in the domain of online social networks and privacy. It also provides a novel theoretical framework for adaptive non-submodular maximization, which has not been studied in the literature. 4) Extensive evaluation through a combination of various tools and methods, including the real-world datasets and applications that will bridge the gap between theory and 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.

过去几十年来，在数据集领域中以前所未有的规模见证了智能数据分析的巨大转变。大数据的分析是计算要求的，饥饿的资源和更复杂的。 随着最新的新兴应用，大多数研究的目标函数已被证明是非管状或非线性的。此外，随着数十亿个尺度数据集的动态存在，例如以在线方式到达的项目，可扩展和基于流的自适应算法，可以快速更新解决方案而不是从头开始更新解决方案。上述所有问题都呼吁采用可扩展和基础的活动挖掘技术，以应对大数据时代的非管状最大化的巨大应用。随着社会对网络空间和计算机技术的日益依赖，对许多新兴应用程序的智能大数据分析的溢价。因此，该项目的成功在几乎所有需要轻巧且近乎最佳的大数据分析的领域都产生了很大的影响。该项目的发现还将丰富有关网络科学，图理论，优化和大数据分析的研究。除了创建新课程外，本科生和高中生还将通过实验平台进行动手活动。针对代表性不足的组和K-1项目的外联事件与高度可扩展的近似算法以及对非屈服和非线性优化类别类别的紧密理论绩效保证。特别是，该项目奠定了新型数据挖掘技术的基础，适用于带有新兴应用程序的大数据的新时代，并推进了基于随机和基的算法设计的研究方面，并具有多种关键创新：1）严格的数学技术来分析和设计针对非单调的非单调最大化类别的高度可扩展的近似算法，这是许多新兴应用的基础。 2）尝试通过桥接非线性优化和组合优化来尝试新的研究方向，从而带来了研究非管状优化的新角度，并对问题结构进行了更深入的了解。 3）针对多个应用程序的新型基于流的活动挖掘，重点是两个通用模型，这些模型统一了在线社交网络和隐私领域的许多优化问题。它还为自适应非管状最大化提供了一种新颖的理论框架，该框架尚未在文献中进行研究。 4）通过各种工具和方法的结合，包括将弥合理论与实践之间差距的现实数据集和应用程序，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来评估的支持，这些奖项通过各种工具和方法进行了广泛的评估。优点和更广泛的影响审查标准。

项目成果

Deep Graph Representation Learning and Optimization for Influence Maximization

• DOI: 10.48550/arxiv.2305.02200
• 发表时间: 2023-05
• 作者: Chen Ling;Junji Jiang;Junxiang Wang;M. Thai;Lukas Xue;James Song;M. Qiu;Liang Zhao

Streaming k-Submodular Maximization under Noise subject to Size Constraint

• 发表时间: 2020-07
• 作者: Lan N. Nguyen;M. Thai

On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network

• 发表时间: 2022-06
• 作者: Hongchang Gao;Bin Gu;M. Thai

Linear Query Approximation Algorithms for Non-monotone Submodular Maximization under Knapsack Constraint

• DOI: 10.48550/arxiv.2305.10292
• 发表时间: 2023-05
• 作者: Canh V. Pham;Tan D. Tran;Dung T. K. Ha;M. Thai

FastHare: Fast Hamiltonian Reduction for Large-scale Quantum Annealing

• DOI: 10.1109/qce53715.2022.00030
• 发表时间: 2022-05
• 期刊: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE)
• 作者: Phuc Thai;M. Thai;Tam Vu;Thang N. Dinh