Localization and Adaptation in Online Learning Through Relaxations

author: Karthik Sridharan, Department of Computer Science, Cornell University
published: Oct. 6, 2014,   recorded: December 2013,   views: 1594

Related Open Educational Resources

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.


The traditional worst-case analysis for online learning problems is often too pessimistic for real world applications. We would like to design adaptive online learning algorithms that enjoy much better (faster rates) regret bounds against "nicer" data sequences while still preserving the worst-case bounds against the worst case data sequences. While in previous works such algorithms have been designed for specific problems, in this talk I shall describe a generic methodology for designing adaptive algorithms for general online learning problems. Specifically I shall introduce the idea of adaptive relaxation and the concept of localization in online learning. Using these concepts I shall provide a general recipe for designing adaptive online learning algorithms for problems. Through examples I shall illustrate the utility of the introduced concepts on several problems. These examples include new adaptive online learning algorithms against iid adversaries and algorithms that can adapt to data geometry.

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: