Stochastic optimization with non-i.id. noise
published: Jan. 25, 2012, recorded: December 2011, views: 3970
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.
We study the convergence of a class of stable online algorithms for stochastic convex optimization in settings where we do not receive independent samples from the distribution over which we optimize, but instead receive samples that are coupled over time. We show the optimization error of the averaged predictor output by any stable online learning algorithm is upper bounded|with high probability|by the average regret of the algorithm, so long as the underlying stochastic process is - or -mixing. We additionally show sharper convergence rates when the expected loss is strongly convex, which includes as special cases linear prediction problems including linear and logistic regression, least-squares SVM, and boosting.
Download slides: nipsworkshops2011_agarwal_noise_01.pdf (356.7 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Write your own review or comment: