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

arXiv: https://arxiv.org/abs/2306.06613 · HTML: https://mlsanity.com/paper/2306.06613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Variants of RMSProp and Adagrad with Logarithmic Regret Bounds](https://mlsanity.com/paper/1706.05507/) (2017)
- [MetaGrad: Multiple Learning Rates in Online Learning](https://mlsanity.com/paper/1604.08740/) (2016)
- [Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization](https://mlsanity.com/paper/1905.05917/) (2019)

## Abstract (arXiv)

The optimal learning rate for adaptive gradient methods applied to λ-strongly convex functions relies on the parameters λ and learning rate η. In this paper, we adapt a universal algorithm along the lines of Metagrad, to get rid of this dependence on λ and η. 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.