Natural language research announcements

["Samdani, Rajhans" <rsamdan2 AT illinois.edu>]

Hi all,

This week we're hosting Prof. Sewoong Oh 
(http://web.engr.illinois.edu/~swoh/) from the Industrial Engineering 
department. Prof. Oh has done a lot of interesting work in Machine Learning, 
amongst other things. Here are the talk details:

When: Friday, Feb 22, 2 pm.

Where: Siebel Center, 3405.

Title: Ranking from Pair-wise Comparisons
 
Abstract:            
The question of aggregating pairwise comparisons to obtain a global ranking 
has been of interest for a very long time: be it ranking of online gamers 
(e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, 
or deciding which product to sell based on transactions. In addition to 
obtaining a ranking, finding 'scores' for each object is of interest for 
understanding the intensity of the preferences. In this talk, I will describe 
a new approach for discovering scores for objects (or items) from pairwise 
comparisons. The algorithm has a natural random walk interpretation over the 
graph of objects with an edge present between a pair of objects if they are 
compared; the stationary probability of this random walk assigns a score to 
each objects. To establish the efficacy of our method, we consider the 
popular Bradley-Terry-Luce (BTL) model and provide an upper bound on the 
finite sample error rate. The number of samples required to learn the score 
well with high probability depends on the structure of the comparisongraph. 
When the Laplacian of the comparison graph has a strictly positive spectral 
gap, this leads to an order-optimal sample complexity. We also provide 
numerical results on real and synthetic data-sets to compare our approach to 
other popular approaches.

Bio: Sewoong Oh is an Assistant Professor of Industrial and Enterprise 
Systems Engineering at UIUC. His research interest is in understanding how to 
extract meaningful information from societal data, such as aggregating 
opinions on social computation platforms like Mechanical Turk, making 
recommendations from rating of individuals, and finding ranking from 
comparisons.

Hope to see you all!
Best,
Rajhans