Greedy Step Averaging: A parameter-free stochastic optimization method

TL;DR

The paper introduces GSA, a parameter-free stochastic optimization algorithm that adaptively determines learning rates without hyperparameter tuning, demonstrating robustness across multiple datasets.

Contribution

GSA is a novel gradient-based method that eliminates the need for manual learning rate tuning, simplifying the optimization process in machine learning.

Findings

GSA performs comparably or better than state-of-the-art methods on 16 datasets.

GSA is robust across various machine learning scenarios.

GSA requires no hyperparameter tuning or additional computational cost.

Abstract

In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems. As a gradient-based optimization method, GSA makes use of the information from the minimizer of a single sample's loss function, and takes average strategy to calculate reasonable learning rate sequence. While most existing gradient-based algorithms introduce an increasing number of hyper parameters or try to make a trade-off between computational cost and convergence rate, GSA avoids the manual tuning of learning rate and brings in no more hyper parameters or extra cost. We perform exhaustive numerical experiments for logistic and softmax regression to compare our method with the other state of the art ones on 16 datasets. Results show that GSA is robust on various scenarios.