Michael Kamp
Effective parallelisation for machine learning
Kamp, Michael; Boley, Mario; Missura, Olana; Gärtner, Thomas
Authors
Mario Boley
Olana Missura
Thomas Gärtner
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 [21] 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.
Citation
Kamp, M., Boley, M., Missura, O., & Gärtner, T. (2017). Effective parallelisation for machine learning
Conference Name | 31st Annual Conference: Neural Information Processing Systems 2017 |
---|---|
End Date | Dec 9, 2017 |
Acceptance Date | Sep 4, 2017 |
Publication Date | Dec 4, 2017 |
Deposit Date | Nov 24, 2017 |
Publicly Available Date | Dec 4, 2017 |
Journal | Advances in Neural Information Processing Systems |
Electronic ISSN | 1049-5258 |
Publisher | Massachusetts Institute of Technology Press |
Peer Reviewed | Peer Reviewed |
Volume | 30 |
Public URL | http://eprints.nottingham.ac.uk/id/eprint/48362 |
Publisher URL | https://papers.nips.cc/paper/7226-effective-parallelisation-for-machine-learning |
Related Public URLs | https://nips.cc/Conferences/2017/Schedule?showEvent=9417 |
Copyright Statement | Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf |
Additional Information | Acknowlegement of acceptance for publication. Effective Parallelisation for Machine Learning. Advances in Neural Information Processing Systems 30 (NIPS 2017). 31st Annual Conference: Neural Information Processing Systems 2017 held 4-9 December 2017, Long Beach, California. |
Files
paper3243 TG.pdf
(350 Kb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
You might also like
Scalable Learning in Reproducing Kernel Kreĭn Spaces
(2019)
Conference Proceeding
Active search for computer-aided drug design
(2018)
Journal Article
Introducing the 'active search' method for iterative virtual screening
(2015)
Journal Article
Interactive knowledge-based kernel PCA
(2014)
Conference Proceeding
Predicting unexpected influxes of players in EVE online
(2014)
Conference Proceeding