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• W2399489790 abstract "We describe a primal-dual framework for the design and analysis of online convex optimization algorithms for {em drifting regret}. Existing literature shows (nearly) optimal drifting regret bounds only for the $ell_2$ and the $ell_1$-norms. Our work provides a connection between these algorithms and the Online Mirror Descent ($omd$) updates; one key insight that results from our work is that in order for these algorithms to succeed, it suffices to have the gradient of the regularizer to be bounded (in an appropriate norm). For situations (like for the $ell_1$ norm) where the vanilla regularizer does not have this property, we have to {em shift} the regularizer to ensure this. Thus, this helps explain the various updates presented in cite{bansal10, buchbinder12}. We also consider the online variant of the problem with 1-lookahead, and with movement costs in the $ell_2$-norm. Our primal dual approach yields nearly optimal competitive ratios for this problem." @default.
• W2399489790 created "2016-06-24" @default.
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• W2399489790 date "2013-09-23" @default.
• W2399489790 modified "2023-09-26" @default.
• W2399489790 title "Fenchel Duals for Drifting Adversaries" @default.
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