TIME: 1:30-2:20 pm,  January 8, 2008

PLACE: CSE 503  

SPEAKER: Marina Meila
         Department of Statistics
	 University of Washington

TITLE: Consensus ranking and exponential models


ABSTRACT:
This talk is concerned with summarizing -- by means of statistical
models -- of data that expresses preferences. This data is typically a
set of rankings of n items by a panel of experts; the simplest summary
is the "consensus ranking", or the "centroid" of the set of
rankings. Such problems appear in many tasks, ranging from combining
voter preferences to boosting of search engines.

We study the problem in its more general form of estimating a
parametric model over permutations, known as the Generalized Mallows
(GM) model. The talk will present a new exact estimation algorithm,
non-polynomial in theory, but tractable in practice. Moreover, the new
algorithm gives insights into what makes consensus ranking hard.

Then we introduce the infinite GM model, corresponding to "rankings"
over an infinite set of items, and show that this model is both
elegant and of practical significance. 

Joint work with: Bhushan Mandhani, Le Bao, Kapil Phadnis, Arthur
Patterson and Jeff Bilmes.