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The optimal learning rate for adaptive gradient methods applied to{\lambda}-strongly convex functions relies on the parameters {\lambda} andlearning rate {\eta}. In this paper, we adapt a universal algorithm along thelines of Metagrad, to get rid of this dependence on {\lambda} and {\eta}. Themain idea is to concurrently run multiple experts and combine their predictionsto a master algorithm. This master enjoys O(d log T) regret bounds.

Parameter-free version of Adaptive Gradient Methods for Strongly-Convex Functions