Accelerating Gradient Boosting Machine

TL;DR

This paper introduces Accelerated Gradient Boosting Machine (AGBM), which incorporates Nesterov's acceleration into GBM, achieving faster convergence with theoretical guarantees and improved empirical performance.

Contribution

It presents the first GBM variant with theoretically-justified accelerated convergence by designing a corrected residual for inexact weak learners.

Findings

AGBM converges faster than traditional GBM.

Numerical experiments show improved training and testing performance.

Theoretical guarantees validate the acceleration approach.

Abstract

Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In this work, we propose Accelerated Gradient Boosting Machine (AGBM) by incorporating Nesterov's acceleration techniques into the design of GBM. The difficulty in accelerating GBM lies in the fact that weak (inexact) learners are commonly used, and therefore the errors can accumulate in the momentum term. To overcome it, we design a "corrected pseudo residual" and fit best weak learner to this corrected pseudo residual, in order to perform the z-update. Thus, we are able to derive novel computational guarantees for AGBM. This is the first GBM type of algorithm with theoretically-justified accelerated convergence rate. Finally we demonstrate with a number of numerical experiments the effectiveness of AGBM over conventional GBM in obtaining a model with good training and/or testing data fidelity.