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- W2952538207 abstract "We present a new optimization-theoretic approach to analyzing Follow-the-Leader style algorithms, particularly in the setting where perturbations are used as a tool for regularization. We show that adding a strongly convex penalty function to the decision rule and adding stochastic perturbations to data correspond to deterministic and stochastic smoothing operations, respectively. We establish an equivalence between the Regularized Leader and the Perturbed Leader up to the smoothness properties. This intuition leads to a new generic analysis framework that recovers and improves the previous known regret bounds of the class of algorithms commonly known as Follow the Perturbed Leader." @default.
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- W2952538207 date "2014-05-23" @default.
- W2952538207 modified "2023-09-27" @default.
- W2952538207 title "Online Linear Optimization via Smoothing" @default.
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