Generalization error bounds for learning to rank: Does the length of document lists matter?

author: Sougata Chaudhuri, Department of Statistics, University of Michigan
published: Sept. 27, 2015,   recorded: July 2015,   views: 30
Categories

See Also:

Download slides icon Download slides: icml2015_chaudhuri_error_bounds_01.pdf (254.2 KB)


Help icon Streaming Video Help

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is no degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under ℓ1 regularization and faster convergence rates if the loss function is smooth.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: