Parameter-free version of Adaptive Gradient Methods for Strongly-Convex Functions
- Resource Type
- Working Paper
- Authors
- Gouda, Deepak; Naveed, Hassan; Kamath, Salil
- Source
- Subject
- Computer Science - Machine Learning
Mathematics - Optimization and Control
- Language
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.