Natural language research announcements

[Yuancheng Tu <ytu AT illinois.edu>]

Can anyone who has KeyNote to convert that file to either PDF or PPT so that 
we can all view it?

Thanks,

Yuancheng

---- Original message ----
>Date: Mon,  4 Oct 2010 17:51:34 -0500 (CDT)
>From: Rajhans Samdani 
><rsamdan2 AT illinois.edu>
>  
>Subject: Re: [cogcomp] 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,
> 
>aiis AT cs.uiuc.edu,
> 
>aistudents AT cs.uiuc.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.
>
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