Improved Strongly Adaptive Online Learning using Coin Betting

10/14/2016

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by   Kwang-Sung Jun, et al.

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This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least √((T)) better, where T is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.