Date of this Version
Advances in Neural Information Processing Systems
We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-varying Gibbs distribution. In the setting of online convex optimization and repeated games, the algorithm yields low regret and presents a novel efficient method for implementing mixture forecasting strategies.
Narayanan, H., & Rakhlin, A. (2010). Random Walk Approach to Regret Minimization. Advances in Neural Information Processing Systems, 23 Retrieved from https://repository.upenn.edu/statistics_papers/468
Date Posted: 27 November 2017