Home/Gradient Boosting on Stochastic Data Streams

Gradient Boosting on Stochastic Data Streams

Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert and J. Andrew (Drew) Bagnell
Conference Paper, International Conference on Artificial Intelligence and Statistics (AISTATS 2017), April, 2017

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the problem of adapting batch gradient boosting for minimizing convex loss functions to online setting where the loss at each iteration is i.i.d sampled from an unknown distribution. To generalize from batch to online, we first introduce the definition of online weak learning edge with which for strongly convex and smooth loss functions, we present an algorithm, Streaming Gradient Boosting (SGB) with exponential shrinkage guarantees in the number of weak learners. We further present an adaptation of SGB to optimize non-smooth loss functions, for which we derive a O(lnN/N) convergence rate. We also show that our analysis can extend to adversarial online learning setting under a stronger assumption that the online weak learning edge will hold in adversarial setting. We finally demonstrate experimental results showing that in practice our algorithms can achieve competitive results as classic gradient boosting while using less computation.

BibTeX Reference
@conference{Hu-2017-17722,
title = {Gradient Boosting on Stochastic Data Streams},
author = {Hanzhang Hu and Wen Sun and Arun Venkatraman and Martial Hebert and J. Andrew (Drew) Bagnell},
booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS 2017)},
keyword = {Boosting, Online Learning},
publisher = {JMLR},
month = {April},
year = {2017},
}
2017-09-15T08:39:58+00:00