Competing With Strategies
Date of this Version
JMLR: Workshop and Conference Proceedings
We study the problem of online learning with a notion of regret defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and for deriving regret-minimization algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate existence of regret-minimization methods that compete with such sets of strategies as: autoregressive algorithms, strategies based on statistical models, regularized least squares, and follow the regularized leader strategies. In several cases we also derive efficient learning algorithms
Han, W., Rakhlin, A., & Sridharan, K. (2013). Competing With Strategies. JMLR: Workshop and Conference Proceedings, 2013 1-27. Retrieved from https://repository.upenn.edu/statistics_papers/132
Date Posted: 27 November 2017
This document has been peer reviewed.