[Compstats] Google-UW ML seminar: Wed May 5th @ 4pm MC5158

Pascal Poupart ppoupart at cs.uwaterloo.ca
Wed May 5 10:31:02 EDT 2010


Quick reminder about David McAllester's talk today @ 4pm in MC5158. 

Also, all grad students are invited to meet with David on Thursday at 
11am in DC2314.

Pascal

Pascal Poupart wrote:
> Hi everyone,
>
> David McAllester (from the Toyota Technological Institute) will be our 
> guest on May 5th and May 6th.  He will give the second Google-UW 
> Machine Learning seminar next week on Wednesday @ 4pm in MC5158.  He 
> is a world leader in Machine Learning.  I encourage everyone to 
> attend.  We will also webcast his talk.  So, if you'd like to see his 
> talk remotely, you can login (up to 30 minutes before the talk) by 
> clicking on the link below to watch David McAllester in real time.  
> Title ans abstract follow below.
>
> cheers,
> Pascal
> ===========
>
> Title: Frequents vs. Bayesians, the PAC-Bayesian synthesis, and 
> support vector machines.
> Speaker: David McAllester (Toyota Technological Institute)
>
> Date: Wednesday May 5th
> Time: 4pm
> Location: MC5158
> Webcast: http://powerlink.powerstream.net/003/02484/live1.asx
> Seminar series website: http://compstats.uwaterloo.ca/google
>
> Abstract:
>
> We will start with a description of the frequentist (objective 
> probability) and Bayesian (subjective probability) positions. We will 
> then describe the PAC-Bayesian theorem which allows for a kind of 
> formal synthesis of the two positions. The talk will then focus on 
> support vector machines as a case study in PAC-Bayesian analysis. We 
> will discuss the "SVM scandal" --- no meaningful formal justification 
> for the hinge loss of soft SVMs has ever been given. We will also 
> apply PAC-Bayesian analysis to recent trends in structural SVMs. 
> Structural SVMs are a way of training the parameters of graphical 
> models and are becoming increasingly popular in areas such as computer 
> vision and natural language processing.
>
> Biography:
>
> Professor McAllester received his B.S., M.S., and Ph.D. degrees from 
> the Massachusetts Institute of Technology in 1978, 1979, and 1987 
> respectively. He served on the faculty of Cornell University for the 
> academic year of 1987-1988 and served on the faculty of MIT from 1988 
> to 1995. He was a member of technical staff at AT&T Labs-Research from 
> 1995 to 2002. He has been a fellow of the American Association of 
> Artificial Intelligence (AAAI) since 1997. Since 2002 he has been 
> Chief Academic Officer at the Toyota Technological Institute at 
> Chicago. He has authored over 90 refereed publications. Professor 
> McAllester's research areas include machine learning, the theory of 
> programming languages, automated reasoning, AI planning, computer game 
> playing (computer chess), computational linguistics and computer 
> vision. A 1991 paper on AI planning proved to be one of the most 
> influential papers of the decade in that area. A 1993 paper on 
> computer game algorithms influenced the design of the algorithms used 
> in the Deep Blue system that defeated Gary Kasparov. A 1998 paper on 
> machine learning theory introduced PAC-Bayesian theorems which combine 
> Bayesian and nonBayesian methods. He is currently part of a team that 
> has scored in the top two places in the PASCAL object detection 
> challenge (computer vision) in 2007, 2008 and 2009.
>

-- 
------------------------
Pascal Poupart
Associate Professor
David R. Cheriton School of Computer Science
University of Waterloo
200 University Avenue West
Waterloo, Ontario
Canada N2L 3G1
------------------------
Web: http://www.cs.uwaterloo.ca/~ppoupart
Email: ppoupart at cs.uwaterloo.ca 
Telephone: 1-519-888-4567x36239 
Fax: 1-519-885-1208
------------------------



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