HDR TRIPODS: Collaborative Research: Institute for Data, Econometrics, Algorithms and Learning

HDR TRIPODS：协作研究：数据、计量经济学、算法和学习研究所

• 批准号: 1934813
• 负责人: Chao Gao
• 金额: $ 15.46万
• 依托单位: University of Chicago
• 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: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934813&HistoricalAwards=false
• 关键词: HDR; TRIPODS; Collaborative; Research; Institute

The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments. The primary activity of the institute will be thematically focused quarters which will coordinate graduate course work with workshops and external visitors. The institute will facilitate collaboration between Chicago-area institutions through a number of initiatives, and across multiple disciplines. Several components of the research agenda have direct applications areas, and the PIs will involve practitioners in development economics, online markets, public policy, as well as data scientists. The research areas supported by the institute focus on three broad themes: (1) High dimensional data analysis, to address algorithmic and statistical challenges in dealing with high dimensional data, and investigate topics like metric embeddings, sketching, and problems in unsupervised learning; (2) Data Science in Strategic Environments, to address computational and information theoretic challenges in econometric models of strategic behavior like inference on high-dimensional structural parameter spaces, dealing with unobserved heterogeneity, partial identification, and machine learning in econometrics; and (3) Machine learning and optimization, to address foundational questions in both continuous and discrete optimization and its use in machine learning including topics like representation learning, robustness in learning, and provable bounds for non-convex optimization. Initially, six research topics will be selected that tie interests across the institutions: inference and data science on networks; theory of deep learning; incentives in shared data infrastructure; robustness in high-dimensional statistics; high-dimensional data analysis; and algorithms for partially identified models. There will be special quarters (fall and spring) where the Institute will bring together investigators, postdocs, and Ph.D. students to focus on one of the topics. In the following quarter (winter and summer) teams will continue research that advance the proposal topics.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.

数据、计量经济学、算法和学习研究所 (IDEAL) 是一所多学科（计算机科学、统计学、经济学、电气工程和运筹学）和多机构（西北大学、芝加哥丰田理工学院和大学）芝加哥）合作研究所，专注于数据科学理论基础的关键方面。 该研究所将支持战略和非战略环境中与机器学习、高维数据分析和优化相关的基础问题的研究。 该研究所的主要活动将是专题宿舍，协调研究生课程工作与研讨会和外部访客。 该研究所将通过一系列举措促进芝加哥地区机构之间跨多个学科的合作。研究议程的几个组成部分都有直接的应用领域，PI 将涉及发展经济学、在线市场、公共政策以及数据科学家的从业者。 该研究所支持的研究领域集中在三大主题：（1）高维数据分析，解决处理高维数据的算法和统计挑战，并研究度量嵌入、草图和无监督学习中的问题等主题； (2) 战略环境中的数据科学，解决战略行为计量经济学模型中的计算和信息论挑战，例如高维结构参数空间的推理、处理计量经济学中未观察到的异质性、部分识别和机器学习； (3) 机器学习和优化，解决连续和离散优化的基本问题及其在机器学习中的应用，包括表示学习、学习的鲁棒性和非凸优化的可证明界限等主题。 最初，将选择六个与各机构利益相关的研究主题：网络推理和数据科学；深度学习理论；共享数据基础设施的激励措施；高维统计的稳健性；高维数据分析；以及部分识别模型的算法。 研究所将设立专门的宿舍（秋季和春季），将研究人员、博士后和博士聚集在一起。学生专注于其中一个主题。 在接下来的季度（冬季和夏季），团队将继续研究推进提案主题。该项目是美国国家科学基金会利用数据革命 (HDR) 大创意活动的一部分。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。

项目成果

SDP Achieves Exact Minimax Optimality in Phase Synchronization

SDP 在相位同步中实现了精确的极小极大最优性

• DOI: 10.1109/tit.2022.3167603
• 发表时间: 2022-08
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Gao, Chao;Zhang, Anderson Y.
• 通讯作者: Zhang, Anderson Y.

Exact Minimax Estimation for Phase Synchronization

相位同步的精确极小极大估计

• DOI: 10.1109/tit.2021.3112712
• 发表时间: 2021-12
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Gao, Chao;Zhang, Anderson Y.
• 通讯作者: Zhang, Anderson Y.

Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures

控制疫情蔓延：精准封城减少经济损失

• DOI: 10.2139/ssrn.3590621
• 发表时间: 2020-05-18
• 期刊: Becker Friedman Institute for Economics Working Paper Series
• 作者: J. Birge;Ozan C;ogan;ogan;Yiding Feng
• 通讯作者: Yiding Feng

Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems

非平稳线性动力系统控制的动态遗憾最小化

• DOI: 10.1145/3508029
• 发表时间: 2022-02
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Luo, Yuwei;Gupta, Varun;Kolar, Mladen
• 通讯作者: Kolar, Mladen

Partial recovery for top-k ranking: Optimality of MLE and SubOptimality of the spectral method

top-k 排序的部分恢复：MLE 的最优性和谱方法的次最优性

• DOI: 10.1214/21-aos2166
• 发表时间: 2022-06
• 期刊: The Annals of Statistics
• 作者: Chen, Pinhan;Gao, Chao;Zhang, Anderson Y.
• 通讯作者: Zhang, Anderson Y.