Collaborative Research: Novel Computational and Statistical Approaches to Prediction and Estimation

协作研究：预测和估计的新颖计算和统计方法

• 批准号: 1521544
• 负责人: Karthik Sridharan
• 金额: $ 17.5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521544&HistoricalAwards=false
• 关键词: Collaborative; Research; Novel; Computational; Statistical

Terabytes of data are collected by companies and individuals every day. These data possess no value unless one can efficiently process them and use them to make decisions. The scale and the streaming nature of data pose both computational and statistical challenges. The objective of this research project is to develop novel approaches to making online, real-time decisions when data are constantly evolving and highly structured. In particular, this project focuses on online prediction problems involving multiple users in dynamic networks. The project also aims to tackle the privacy issues arising in such multi-user scenarios.In recent years, it was shown that a majority of online machine learning algorithms can be viewed as solutions to approximate dynamic programming (ADP) problems that incorporate one additional datum per step. Along with directly addressing the computational concerns, the ADP framework also provides guaranteed performance on prediction problems. This project is to use and extend the ADP framework to develop prediction algorithms that simultaneously address the issues of computation, robustness, non-stationarity, privacy, and multiplicity of users.

公司和个人每天都会收集数TB的数据。这些数据没有任何价值，除非人们能够有效地处理它们，并使用它们来做出决策。数据的规模和流特性给计算和统计带来了挑战。该研究项目的目标是开发新的方法，以便在数据不断演变和高度结构化的情况下做出在线实时决策。这个项目特别关注动态网络中涉及多个用户的在线预测问题。该项目还旨在解决这种多用户场景中出现的隐私问题。近年来，研究表明，大多数在线机器学习算法可以被视为每一步包含一个额外数据的近似动态规划(ADP)问题的解决方案。除了直接解决计算问题，ADP框架还在预测问题上提供了有保证的性能。这个项目是使用和扩展ADP框架来开发预测算法，同时解决计算、稳健性、非平稳性、隐私和用户多样性的问题。