Online Learning, Regret Minimization, and Game Theory
published: May 7, 2008, recorded: March 2008, views: 26174
Report a problem or upload filesIf 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.
Watch videos: (click on thumbnail to launch)
The first part of tha tutorial will discuss adaptive algorithms for making decisions in uncertain environments (e.g., what route should I take to work if I have to decide before I know what traffic will like today?) and connections to central concepts in game theory (e.g., what can we say about how traffic will behave overall if everyone is adapting their behavior in such a way?). He will discuss the notions of external and internal regret, algorithms for "combining expert advice" and "sleeping experts" problems, algorithms for implicitly specified problems, and connections to game-theoretic notions of Nash and correlated equilibria. The second part of tha tutorial will be about some recent work on learning with similarity functions that are not necessarily legal kernels. The high-level question here is: if you have a measure of similarity between data points, how closely related does it have to be to your classification problem in order to be useful for learning?
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !