[Lisa_seminaires] NEW TIME! UdeM-McGill-MITACS machine learning seminar Fri March 14th, 12:00pm, MC437

Hugo Larochelle larocheh at iro.umontreal.ca
Ven 7 Mar 10:07:43 EST 2008


Next week's seminar (see http://www.iro.umontreal.ca/article.php3? 
id_article=107&lang=en):


Discriminative Methods with Structure


by Simon Lacoste-Julien,
Computer Science
University of California

Location: McConnell Engineering Building (McGill), room 437
Time: March 14th 2008, 12h00

Real world problems such as machine translation involve complex  
dependencies. Generative models have provided an elegant and flexible  
framework to model those dependencies, but they appear to lack  
robustness to model misspecification compared to discriminative  
models for classification. In this talk, we present methods for  
leveraging the advantages of generative models in the discriminative  
framework.

In the first part of the talk, we tackle the word alignment problem  
from natural language processing. We formulate it as a weighted  
bipartite matching problem and show how to learn the weights by using  
a large-margin approach for structured prediction. By providing a  
flexible discriminative modeling framework, we were able to cut the  
Alignment Error Rate in half compared to the previous best performing  
generative models for word alignment.

In the second part of the talk, we study probabilistic topic models  
which have been popular for modeling latent structures in text  
documents (as bag of words) or images (as bag of visual words). They  
are usually trained as generative models with maximum likelihood  
estimation, though this could be suboptimal if one is interested in  
doing classification. In contrast, we present a discriminative  
version of the Latent Dirichlet Allocation (LDA) model which attempts  
to uncover the latent structure in the documents while optimizing its  
predictive power for the task of classification. We present results  
in the domains of document classification and scene categorization.

(joint work with Fei Sha, Ben Taskar, Dan Klein and Michael I. Jordan)


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