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

• URL: http://arxiv.org/abs/2306.06613v2
• Date: Sat, 15 Jul 2023 03:21:29 GMT
• Title: Parameter-free version of Adaptive Gradient Methods for Strongly-Convex Functions
• Authors: Deepak Gouda, Hassan Naveed, Salil Kamath
• Abstract summary: In this paper, we adapt a universal algorithm along the lines of Metagrad, to get rid of this dependence on lambda and eta. The main idea is to concurrently run multiple experts and combine their predictions to a master algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The optimal learning rate for adaptive gradient methods applied to {\lambda}-strongly convex functions relies on the parameters {\lambda} and learning rate {\eta}. In this paper, we adapt a universal algorithm along the lines of Metagrad, to get rid of this dependence on {\lambda} and {\eta}. The main idea is to concurrently run multiple experts and combine their predictions to a master algorithm. This master enjoys O(d log T) regret bounds.

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