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Recently, there has been a growing research interest in the analysis of dynamic regret, which measures the performance of an online learner against a sequence of local minimizers.
Tracking the best expert
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Martin Zinkevich · 2003
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Introductory lectures on convex optimization: a basic course , volume 87 of Applied optimization
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Logarithmic regret algorithms for online convex optimization
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Linear convergence of first order methods for non-strongly convex optimization
I. Necoara, Yu. Nesterov, and F. Glineur · 2015
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Online optimization in dynamic environments: Improved regret rates for strongly convex problems
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Linear convergence of variance-reduced stochastic gradient without strong convexity
Pinghua Gong and Jieping Ye · 2014
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Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, and Ambuj Tewari
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Jacob Abernethy, Elad Hazan, and Alexander Rakhlin
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Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient
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