no code implementations • 7 Mar 2016 • Dean Foster, Satyen Kale, Howard Karloff
We consider the online sparse linear regression problem, which is the problem of sequentially making predictions observing only a limited number of features in each round, to minimize regret with respect to the best sparse linear regressor, where prediction accuracy is measured by square loss.