Natural language research announcements

[Yonatan Bisk <bisk1 AT illinois.edu>]

Lecture Details:

When: Today @ 2pm

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).


Speaker:
Tamir Hazan: http://ttic.uchicago.edu/~tamir/Welcome.html @ TTIC