@inproceedings{
choudhury2024remove,
title={Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad},
author={Sayantan Choudhury and Nazarii Tupitsa and Nicolas Loizou and Samuel Horv{\'a}th and Martin Tak{\'a}{\v{c}} and Eduard Gorbunov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=EdG59dnOzN}
}
Adaptive methods are extremely popular in machine learning as they make learning rate tuning less expensive. This paper introduces a novel optimization algorithm named KATE, which presents a scale-invariant adaptation of the well-known AdaGrad algorithm. We prove the scale-invariance of KATE for the case of Generalized Linear Models. Moreover, for general smooth non-convex problems, we establish a convergence rate of  $O((\log T)/\sqrt{T})$ for KATE, matching the best-known ones for AdaGrad and Adam. We also compare KATE to other state-of-the-art adaptive algorithms Adam and AdaGrad in numerical experiments with different problems, including complex machine learning tasks like image classification and text classification on real data. The results indicate that KATE consistently outperforms AdaGrad and matches/surpasses the performance of Adam in all considered scenarios.
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Sayantan Choudhury Nazarii Tupitsa Nicolas Loizou Samuel Horváth Martin Takáč Eduard Gorbunov