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MrPC: causal structure learning in distributed systems
conference contribution
posted on 2020-01-01, 00:00 authored by Thin NguyenThin Nguyen, Duc Thanh NguyenDuc Thanh Nguyen, T D Le, Svetha VenkateshSvetha VenkateshPC algorithm (PC) – named after its authors, Peter and Clark – is an advanced constraint based method for learning causal structures. However, it is a time-consuming algorithm since the number of independence tests is exponential to the number of considered variables. Attempts to parallelise PC have been studied intensively, for example, by distributing the tests to all computing cores in a single computer. However, no effort has been made to speed up PC through parallelising the conditional independence tests into a cluster of computers. In this work, we propose MrPC, a robust and efficient PC algorithm, to accelerate PC to serve causal discovery in distributed systems. Alongside with MrPC, we also propose a novel manner to model non-linear causal relationships in gene regulatory data using kernel functions. We evaluate our method and its variants in the task of building gene regulatory networks. Experimental results on benchmark datasets show that the proposed MrPCgains up to seven times faster than sequential PC implementation. In addition, kernel functions outperform conventional linear causal modelling approach across different datasets.
History
Event
Neural Information Processing. International Conference (27th : 2020 : Online from Bangkok, Thailand)Volume
1332Series
Neural Information Processing International ConferencePagination
87 - 94Publisher
SpringerLocation
Online from Bangkok, ThailandPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2020-11-18End date
2020-11-22ISSN
1865-0929eISSN
1865-0937ISBN-13
9783030638191Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
H Yang, K Pasupa, A Leung, J Kwok, J Chan, I KingTitle of proceedings
ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020Usage metrics
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