@misc{hsu2011parallel,
author = {Hsu, Daniel and Karampatziakis, Nikos and Langford, John and Smola, Alex J.},
title = {Parallel Online Learning},
howpublished = {https://arxiv.org/abs/1103.4204},
year = {2011},
month = {March},
abstract = {In this work we study parallelization of online learning, a core primitive in machine learning. In a parallel environment all known approaches for parallel online learning lead to delayed updates, where the model is updated using out-of-date information. In the worst case, or when examples are temporally correlated, delay can have a very adverse effect on the learning algorithm. Here, we analyze and present preliminary empirical results on a set of learning architectures based on a feature sharding approach that present various tradeoffs between delay, degree of parallelism, representation power and empirical performance.},
url = {http://approjects.co.za/?big=en-us/research/publication/parallel-online-learning/},
}