Robust Large-Scale Machine Learning in the Cloud

KDD, pp. 1125-1134, 2016.

Cited by: 19|Bibtex|Views104|DOI:https://doi.org/10.1145/2939672.2939790
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Other Links: dblp.uni-trier.de|dl.acm.org|academic.microsoft.com

Abstract:

The convergence behavior of many distributed machine learning (ML) algorithms can be sensitive to the number of machines being used or to changes in the computing environment. As a result, scaling to a large number of machines can be challenging. In this paper, we describe a new scalable coordinate descent (SCD) algorithm for generalized ...More

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