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.
