CIF: Small: Collaborative Research: Rank Aggregation with Heterogeneous Information Sources: Efficient Algorithms and Fundamental Limits

CIF：小型：协作研究：异构信息源的排名聚合：高效算法和基本限制

基本信息

批准号：
1911168
负责人：
Quanquan Gu
金额：
$ 25万
依托单位：
University of California-Los Angeles
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：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911168&HistoricalAwards=false
关键词：
CIF Small Collaborative Research Rank

项目摘要

While advances in the ability to collect and store data have made large data sets commonplace, these data sets increasingly consist of information obtained from different sources with various data types and properties that impede the ability to extract knowledge and make decisions. This project focuses on inferring the ranking of a set of objects from heterogeneous datasets with arbitrary noise, which is also known as rank aggregation with heterogeneous information sources. The developed algorithms will be made publicly available as open source software tools, and will significantly expand the applicability of rank aggregation to real-world problems, such as data fusion, information retrieval, crowd-sourcing, recommendation systems, as well as social choice and voting. This project will also provide educational and training opportunities and exposure to sophisticated statistical tools, rigorous theoretical analysis, and the empirical work of extracting knowledge from large heterogeneous data sets. In this project, based on statistical models of data, efficient and scalable rank aggregation algorithms for various settings will be developed along with performance guarantees and fundamental limits, in three complementary research thrusts. First, it will develop rank aggregation algorithms based on flexible latent probabilistic models that exploit side information and allow both ordinal and numerical data types. It will also provide information-theoretic lower bounds on the performance of such algorithms. Second, it will design robust algorithms for latent probabilistic models in which the unknown parameters are a superposition of many structured parameters, and models in which data can be corrupted by arbitrary noise. Finally, the problem of inferring a ranking through interactive bandit algorithms will be studied. This project aims to push the frontier of rank aggregation research, and can potentially advance research in machine learning, nonconvex optimization and information theory.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.

虽然收集和存储数据能力的进步使大型数据集变得司空见惯，但这些数据集越来越多地包含从不同来源获得的信息，这些信息具有各种数据类型和属性，阻碍了提取知识和做出决策的能力。该项目的重点是从具有任意噪声的异构数据集中推断一组对象的排名，这也称为异构信息源的排名聚合。开发的算法将作为开源软件工具公开提供，并将显着扩展排名聚合对现实世界问题的适用性，例如数据融合、信息检索、众包、推荐系统以及社会选择和决策。表决。该项目还将提供教育和培训机会，以及接触复杂的统计工具、严格的理论分析以及从大型异构数据集中提取知识的实证工作。在该项目中，基于数据统计模型，将在三个互补的研究重点中开发适用于各种设置的高效且可扩展的排名聚合算法以及性能保证和基本限制。首先，它将开发基于灵活的潜在概率模型的排名聚合算法，该模型利用辅助信息并允许序数和数值数据类型。它还将为此类算法的性能提供信息论下限。其次，它将为潜在概率模型（其中未知参数是许多结构化参数的叠加）以及数据可能被任意噪声破坏的模型设计鲁棒算法。最后，将研究通过交互式老虎机算法推断排名的问题。该项目旨在推动排名聚合研究的前沿，并有可能推进机器学习、非凸优化和信息论方面的研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，认为值得支持审查标准。