Statistics Papers
Document Type
Conference Paper
Date of this Version
2009
Publication Source
Conference on Learning Theory
Abstract
We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O*(√T)regret. The setting is a natural generalization of the nonstochastic multiarmed bandit problem, and the existence of an efficient optimal algorithm has been posed as an open problem in a number of recent papers. We show how the difficulties encountered by previous approaches are overcome by the use of a self-concordant potential function. Our approach presents a novel connection between online learning and interior point methods.
Recommended Citation
Abernethy, J. D., Hazan, E., & Rakhlin, A. (2009). Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. Conference on Learning Theory, Retrieved from https://repository.upenn.edu/statistics_papers/110
Date Posted: 27 November 2017
Comments
At the time of publication, author Alexander Rakhlin was affiliated with the University of California, Berkeley. Currently, he is a faculty member at the Statistics Department at the University of Pennsylvania.