Natural language research announcements

[Rajhans Samdani <rsamdan2 AT illinois.edu>]

Some of you have asked for the slides for Amir's talk. They can be found at 
http://www.cs.huji.ac.il/~gamir/uiuc10_small.key.
As you can see, it's in the keynote format.
Best,
Rajhans

---- Original message ----
>Date: Fri,  1 Oct 2010 12:34:35 -0500 (CDT)
>From: Rajhans Samdani 
><rsamdan2 AT illinois.edu>
>  
>Subject: AIIS talk by Amir Globerson at 2pm  
>To: 
>theorycs AT cs.uiuc.edu,
> 
>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,
> 
>eyal AT cs.uiuc.edu,
> 
>aiis AT cs.uiuc.edu,
> 
>aistudents AT cs.uiuc.edu,
> "Girju, Corina R" 
><girju AT illinois.edu>
>
>Hi all, 
>
>Just a gentle reminder regarding today's talk by Amir Globerson 
>(http://www.cs.huji.ac.il/~gamir/) from Hebrew University. The talk is in 
>3405 SC at 2 
>pm. Following are the title and abstract of his talk.
>
>Title: Learning with Approximate inference - From LP Relaxations to 
>Pseudo-Max 
>Approaches 
>
>Abstract: 
>Supervised learning problems often involve the prediction of complex 
>structure labels, 
>such as sequences (e.g., POS tagging) or trees (e.g., dependency parsing). 
>To 
>achieve high accuracy in these tasks, one is often interested in introducing 
>complex 
>dependencies between label parts. However, this can result in prediction 
>problems 
>that are NP hard. A natural approach in these cases is to use tractable 
>approximations of the prediction problem. 
>In this talk I will present our recent work on using approximate inference 
>for structured 
>prediction tasks. I will describe linear programming (LP) relaxations for 
>the problem, 
>and show highly scalable algorithms for learning using these relaxations. I 
>will next 
>introduce a simpler approach, called ‘psuedo-max’  learning, and show that 
>it is 
>consistent for separable problems under certain conditions, and has 
>empirical 
>performance that is similar to LP relaxations. 
>I will conclude by addressing the problem of finding the K best solutions in 
>such 
>problems, and show a new class of relaxations that has theoretical 
>guarantees and 
>works well in practice. 
>
>Base on joint work with: Ofer Meshi, David Sontag, Menachem Fromer and Tommi 
>Jaakkola 
>
>
>Hoping to see you there. 
>Best, 
>Rajhans 
>
>
>Rajhans Samdani,
>Graduate Student,
>Dept. of Computer Science,
>University of Illinois at Urbana-Champaign.


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