On the Computational and Statistical Interface and "BIG DATA"
published: July 15, 2014, recorded: June 2014, views: 1344
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the statistical and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level-in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results or concentration theorems can be invoked. We present several research vignettes on topics at the computation/statistics interface, an interface that we aim to characterize in terms of theoretical tradeoffs between statistical risk, amount of data and "externalities" such as computation, communication and privacy.
[Joint work with Venkat Chandrasekaran, John Duchi, Martin Wainwright and Yuchen Zhang.]
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !