*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
TITLE: Non-Parametric Modeling of Partially Ranked Data
SPEAKER: Dr. Guy Lebanon
ABSTRACT:
Statistical models on full and partial rankings of n items are often of limited practical use for large n due to computational consideration. We explore the use of non-parametric models for partially ranked data and derive computationally efficient procedures for their use for large n. The derivations are largely possible through combinatorial and algebraic manipulations based on the lattice of partial rankings. A bias-variance analysis and an experimental study demonstrate the applicability of the
proposed method.
Bio:
Guy Lebanon is an assistant professor of computing at the Georgia Institute of Technology. His main research area is statistical modeling and visualization of high dimensional discrete data such as text documents and partially ranked data. Additional research interests include privacy preservation in databases and social networks and the
use of non-Euclidean geometry in machine learning.
Prior to his current appoitnment at Georgia Tech, Dr. Lebanon was an assistant professor of statistics and electrical and computer engineering at Purdue University. He recieved his PhD in 2005 from Carnegie Mellon University and BA, MS degrees from Technion - Israel Institute of Technology, all in computer science.
Prof. Lebanon received the NSF CAREER Award, Purdue's Teaching for Tomorrow Award, the 2004 LTI SRS Best Presentation Award and is a Siebel scholar.
