WNCG Seminar Series: Individualized Rank Aggregation

Friday, October 30, 2015
UTA 7.532
In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not want a single ranking and instead opt for individual rankings. We study a version of the problem known as collaborative ranking. In this problem we assume that individual users provide us with pairwise preferences (for example purchasing one item over another). From those preferences we wish to obtain rankings on items that the users have not had an opportunity to explore. We provide a theoretical justification for a nuclear norm regularized optimization procedure, and provide high-dimensional scaling results that show how the error in estimating user preferences behaves as the number of observations increase.
Watch the full presentation online on the WNCG YouTube Channel. 


Assistant Professor
Yale University

Sahand Negahban is currently an Assistant Professor in the Statistics Department at Yale University. Prior to that he worked with Prof. Devavrat Shah at MIT as a postdoc and Prof. Martin J. Wainwright at UC Berkeley as a graduate student.

The focus of Prof. Negahban's research is to develop theoretically sound methods, which are both computationally and statistically efficient, for extracting information from large datasets. A salient feature of his work has been to understand how hidden low-complexity struc- ture in large datasets can be used to develop computationally and statistically efficient methods for extracting meaningful information for high-dimensional estimation problems. His work borrows from and improves upon tools of statistical signal processing, machine learning, probability and convex optimization.