[Colloquium] Sindhwani talk today 12:15 at TTI

[Meridel Trimble]

TOYOTA TECHNOLOGICAL INSTITUTE TALK

Speaker: Vikas Sindhwani 
Speaker’s homepage: http://www.cs.uchicago.edu/~vikass

Title: Manifold Regularization : A Geometric Framework for Learning from 
Examples.

Time: Tuesday, September 14th, 12:15pm
Place: TTI-C conference room (1427 E. 60th St. – 2nd Floor)
Pizza lunch provided

Abstract: We propose a family of learning algorithms based on a new form of 
regularization that allows us to exploit the geometry of the marginal 
distribution.  We focus on a semi-supervised framework that incorporates 
labeled and unlabeled data in a general-purpose learner.  Some 
transductive graph learning algorithms and standard methods including 
Support Vector Machines and Regularized Least Squares can be obtained as 
special cases.  We utilize properties of Reproducing Kernel Hilbert spaces 
to prove new Representer theorems that provide theoretical basis for the 
algorithms.  As a result (in contrast to purely graph based approaches) we 
obtain a natural out-of-sample extension to novel examples and so are able 
to handle both transductive and truly semi-supervised settings.  We 
present experimental evidence suggesting that our semi-supervised 
algorithms are able to use unlabeled data effectively. Finally, we briefly 
discuss unsupervised and fully supervised learning within our general 
framework. 

This is joint work with Mikhail Belkin and Partha Niyogi.

If you have questions, or would like to meet the speaker, please contact 
Meridel at 4-9873 or mtrimble at tti-c.org