2017
Kamp, Michael; Boley, Mario; Missura, Olana; GĂ€rtner, Thomas
Effective Parallelisation for Machine Learning Proceedings Article
In: Advances in Neural Information Processing Systems, pp. 6480â6491, 2017.
Abstract | Links | BibTeX | Tags: decentralized, distributed, machine learning, parallelization, radon
@inproceedings{kamp2017effective,
title = {Effective Parallelisation for Machine Learning},
author = {Michael Kamp and Mario Boley and Olana Missura and Thomas GĂ€rtner},
url = {http://papers.nips.cc/paper/7226-effective-parallelisation-for-machine-learning.pdf},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {Advances in Neural Information Processing Systems},
pages = {6480--6491},
abstract = {We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications. In contrast to other parallelisation techniques, it can be applied to a broad class of learning algorithms without further mathematical derivations and without writing dedicated code, while at the same time maintaining theoretical performance guarantees. Moreover, our parallelisation scheme is able to reduce the runtime of many learning algorithms to polylogarithmic time on quasi-polynomially many processing units. This is a significant step towards a general answer to an open question on efficient parallelisation of machine learning algorithms in the sense of Nick's Class (NC). The cost of this parallelisation is in the form of a larger sample complexity. Our empirical study confirms the potential of our parallelisation scheme with fixed numbers of processors and instances in realistic application scenarios.},
keywords = {decentralized, distributed, machine learning, parallelization, radon},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications. In contrast to other parallelisation techniques, it can be applied to a broad class of learning algorithms without further mathematical derivations and without writing dedicated code, while at the same time maintaining theoretical performance guarantees. Moreover, our parallelisation scheme is able to reduce the runtime of many learning algorithms to polylogarithmic time on quasi-polynomially many processing units. This is a significant step towards a general answer to an open question on efficient parallelisation of machine learning algorithms in the sense of Nick's Class (NC). The cost of this parallelisation is in the form of a larger sample complexity. Our empirical study confirms the potential of our parallelisation scheme with fixed numbers of processors and instances in realistic application scenarios.
