Natural language research announcements

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

Hi all,

This Friday we're hosting Prof. Tamir Hazan 
(http://ttic.uchicago.edu/~tamir/Welcome.html) from TTIC. Prof. Hazan has 
done some very interesting working machine learning and computer vision. 

He will also hold meetings with interested researchers from 10 am to 5 pm on 
the same day (i.e. Friday, April 13.) If you're interested in meeting him, 
please email me or 
bisk1 AT illinois.edu
 with a preferred slot.


Lecture Details:

When: Friday, April 13. 

Where: 3405, Siebel Center.

Title:
Estimating the Partition Function with Random Maximum A-Posteriori 
Perturbations

Abstract:
Learning and inference in complex models drives much of the research in 
machine learning applications, from computer vision, natural language 
processing, to computational biology. The inference problem in such cases 
involves assessing the likelihood of possible structures, whether objects, 
parsers, or molecular structures. Although it is often feasible to only find 
the most likely or maximum a-posteriori (MAP) assignment rather than 
considering all possible assignments, MAP inference is limited when there are 
other likely assignments. In a fully probabilistic treatment, all possible 
alternative assignments are considered thus requiring summing over the 
assignments with their respective weights -- evaluating the partition 
function -- which is considerably harder. The main surprising result of our 
work is that MAP inference (maximization) can be used to approximate and 
bound the partition function (weighted counting). Specifically, we relate the 
partition function to the expected MAP value of perturbations, thus we are 
able for the first time to directly use efficient MAP solvers such as 
graph-cuts in calculating the partition function. This approach leads to a 
new approximate inference framework, which supports efficient sampling and 
statistical approximations whenever the MAP assignment can be recovered 
efficiently. The approach excels in regimes where there are several but not 
exponentially many prominent assignments. For example, this happens in cases 
where observations carry strong signals (local evidence) but are also guided 
by strong consistency constraints (couplings).

Hope to see y'all

Thanks!
Rajhans