Skip to Content.
Sympa Menu

nl-uiuc - [nl-uiuc] AIIS seminar talk on March 12'th

nl-uiuc AT lists.cs.illinois.edu

Subject: Natural language research announcements

List archive

[nl-uiuc] AIIS seminar talk on March 12'th


Chronological Thread 
  • From: Rajhans Samdani <rsamdan2 AT illinois.edu>
  • To: nl-uiuc AT cs.uiuc.edu, aivr AT cs.uiuc.edu, dais AT cs.uiuc.edu, cogcomp AT cs.uiuc.edu, vision AT cs.uiuc.edu, krr-group AT cs.uiuc.edu, aiis AT cs.uiuc.edu
  • Subject: [nl-uiuc] AIIS seminar talk on March 12'th
  • Date: Mon, 8 Mar 2010 18:08:52 -0600 (CST)
  • List-archive: <http://lists.cs.uiuc.edu/pipermail/nl-uiuc>
  • List-id: Natural language research announcements <nl-uiuc.cs.uiuc.edu>

Dear Faculty and Students,

In the AIIS seminar (http://l2r.cs.uiuc.edu/~cogcomp/aiis/), this week we
have a talk related to structured prediction by Shankar Vembu who is doing
his Post Doc in the Cognitive Computation Lab under Prof. Dan Roth. The talk
is scheduled at 2 pm, March 12'th (i.e. Friday) in room no. 3405 SC. Attached
with this mail are the title and the abstract of the talk.

Title:
Learning to Predict Combinatorial Structures

Abstract:
The major challenge in designing a discriminative learning
algorithm for predicting structured data is to address the
computational issues arising from the exponential size of the output
space. Existing algorithms make different assumptions to ensure
efficient, polynomial time estimation of model parameters. For several
combinatorial structures, including cycles, partially ordered sets,
permutations and other graph classes, these assumptions do not hold.
In this work, we address the problem of designing learning algorithms
for predicting combinatorial structures by introducing two new
assumptions: (i) The first assumption is that a particular counting
problem can be solved efficiently. The consequence is a generalization
of the classical ridge regression for structured prediction. (ii) The
second assumption is that a particular sampling problem can be solved
efficiently. The consequence is a new technique for designing and
analyzing probabilistic structured prediction models. These results
can be applied to solve several complex learning problems including
but not limited to multi-label classification, multi-category
hierarchical classification, and label ranking.

Hoping to see you all there,
Regards,
Rajhans


Rajhans Samdani,
Graduate Student,
Dept. of Computer Science,
University of Illinois at Urbana-Champaign.



  • [nl-uiuc] AIIS seminar talk on March 12'th, Rajhans Samdani, 03/08/2010

Archive powered by MHonArc 2.6.16.

Top of Page