[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 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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