Carath\'eodory Sampling for Stochastic Gradient Descent

TL;DR

This paper introduces a novel measure reduction technique inspired by Carathéodory's theorem to improve the scalability of stochastic gradient descent by reducing variance and computational cost.

Contribution

It proposes a new measure reduction approach for SGD that adaptively reduces variance and computational complexity, enabling efficient optimization in high-dimensional settings.

Findings

The method effectively reduces variance in gradient estimates.

Experimental results show improved scalability and efficiency.

The approach outperforms traditional SGD variants in large-scale experiments.

Abstract

Many problems require to optimize empirical risk functions over large data sets. Gradient descent methods that calculate the full gradient in every descent step do not scale to such datasets. Various flavours of Stochastic Gradient Descent (SGD) replace the expensive summation that computes the full gradient by approximating it with a small sum over a randomly selected subsample of the data set that in turn suffers from a high variance. We present a different approach that is inspired by classical results of Tchakaloff and Carath\'eodory about measure reduction. These results allow to replace an empirical measure with another, carefully constructed probability measure that has a much smaller support, but can preserve certain statistics such as the expected gradient. To turn this into scalable algorithms we firstly, adaptively select the descent steps where the measure reduction is carried out; secondly, we combine this with Block Coordinate Descent so that measure reduction can be done very cheaply. This makes the resulting methods scalable to high-dimensional spaces. Finally, we provide an experimental validation and comparison.