FRG: Collaborative Research: Generative Learning on Unstructured Data with Applications to Natural Language Processing and Hyperlink Prediction

FRG：协作研究：非结构化数据的生成学习及其在自然语言处理和超链接预测中的应用

• 批准号: 1952539
• 负责人: Xiaotong Shen
• 金额: $ 30万
• 依托单位: University of Minnesota-Twin Cities
• 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: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952539&HistoricalAwards=false
• 关键词: FRG; Collaborative; Research; Generative; Learning

This project addresses the pressing needs of analyzing “big” unstructured data and tackles some artificial intelligence questions from the statistical perspective, which requires the focused and synergistic efforts of a collaborative team. Specifically, the project develops generative models for statistical learning and leverages dependence relations modeled by graphical models in hyperlink prediction, which are applicable to topic sentence generation and protein structure identification. It will lead to a substantial improvement in the accuracy of generative learning based on numerical embeddings, particularly in topic sentence generation and hyperlink prediction. The integrated program of research and education will have significant impacts on machine learning and data science, social and political sciences, and biomedical and genomic research, among others. The project requires extensive algorithm and software development for natural language processing and multimedia data integration. The PIs, their postdocs, and students will develop innovative computational algorithms and software for the analysis of large-scale unstructured complex data. The advanced computational tools will be disseminated to facilitate technology transfer. The project will address some fundamental issues in two important areas of unstructured data analysis in machine learning and intelligence. In particular, the proposed research will develop a statistical framework for generative learning, which is primarily motivated by applications for unstructured data, namely topic sentence generation and high-order hyperlink prediction. The research will develop powerful generative methods for generating instances or examples to describe and interpret the corresponding learning model. Moreover, it will develop network models for modeling high-order interactions and relations of units by identifying hidden structures in networks. It will proceed in two areas: (1) instance generation and topic sentence generation; (2) hyperlink prediction for multiway relations in hypergraphs. In the first area, instance generation, particularly sentence generation, will be performed collaboratively with numerical embeddings in categorization and regression. In the second area, hyperlinks will be predicted based on observed pairwise as well as unobserved high-order relations, characterized by graphical models with hidden structures. Special effort will be devoted to inverse learning, the integration of data from multiple sources, and extracting latent structures of networks. Finally, the research will develop computational tools and design practical methods that have desirable statistical properties.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.

该项目解决了分析“大”非结构化数据的迫切需求，并从统计角度解决了一些人工智能问题，这需要协作团队的专注和协同工作。具体而言，该项目开发了用于统计学习的生成模型，并利用超链接预测中的图形模型建模的依赖关系，适用于主题句生成和蛋白质结构识别。这将导致基于数值嵌入的生成学习的准确性的实质性提高，特别是在主题句生成和超链接预测方面。研究和教育的综合计划将对机器学习和数据科学，社会和政治科学，生物医学和基因组研究等产生重大影响。该项目需要广泛的算法和软件开发的自然语言处理和多媒体数据集成。PI，他们的博士后和学生将开发用于分析大规模非结构化复杂数据的创新计算算法和软件。将传播先进的计算工具，以促进技术转让。该项目将解决机器学习和智能中非结构化数据分析两个重要领域的一些基本问题。特别是，拟议的研究将开发一个统计框架生成学习，这主要是由非结构化数据的应用程序，即主题句生成和高阶超链接预测的动机。该研究将开发强大的生成方法，用于生成实例或示例来描述和解释相应的学习模型。 此外，它将开发网络模型，通过识别网络中的隐藏结构来模拟高阶相互作用和单元关系。主要从两个方面进行：（1）实例生成和主题句生成;（2）超图中多向关系的超链接预测。在第一个领域，实例生成，特别是句子生成，将与分类和回归中的数值嵌入协同进行。在第二个领域中，超链接将根据观察到的成对以及未观察到的高阶关系进行预测，其特征在于具有隐藏结构的图形模型。特别的努力将致力于逆向学习，整合来自多个来源的数据，并提取网络的潜在结构。 最后，研究将开发计算工具和设计具有理想统计特性的实用方法。该奖项反映了NSF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

Distribution-Invariant Differential Privacy

• DOI: 10.1016/j.jeconom.2022.05.004
• 发表时间: 2021-11
• 期刊: Journal of econometrics
• 影响因子: 6.3
• 作者: Xuan Bi;Xiaotong Shen

Embedding Learning

• DOI: 10.1080/01621459.2020.1775614
• 发表时间: 2020-07-20
• 期刊: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• 影响因子: 3.7
• 作者: Dai, Ben;Shen, Xiaotong;Wang, Junhui
• 通讯作者: Wang, Junhui

A hierarchical ensemble causal structure learning approach for wafer manufacturing

用于晶圆制造的分层集成因果结构学习方法

• DOI: 10.1007/s10845-023-02188-z
• 发表时间: 2023
• 期刊: Journal of Intelligent Manufacturing
• 影响因子: 8.3
• 作者: Yang, Yu;Bom, Sthitie;Shen, Xiaotong
• 通讯作者: Shen, Xiaotong

Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation

具有统计保证解释的数据自适应判别特征定位

• 发表时间: 2023
• 期刊: Annals of applied statistics
• 影响因子: 1.8
• 作者: Dai, B.;Shen, X.;Li, C.;Chen, C.;Pan, W.
• 通讯作者: Pan, W.

Inference for a large directed graphical model with interventions.

具有干预措施的大型有向图模型的推理。

• 发表时间: 2023
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Li, C.;Shen, X.;Pan, W.
• 通讯作者: Pan, W.