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Data Driven Resource Allocation for Distributed Learning

Travis Dick,Mu Li,Krishna Pillutla,Colin White,Maria-Florina Balcan,Alex Smola

Published 2015 in International Conference on Artificial Intelligence and Statistics

ABSTRACT

In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propose data dependent dispatching that takes advantage of such structure. We present an in-depth analysis of this model, providing new algorithms with provable worst-case guarantees, analysis proving existing scalable heuristics perform well in natural non worst-case conditions, and techniques for extending a dispatching rule from a small sample to the entire distribution. We overcome novel technical challenges to satisfy important conditions for accurate distributed learning, including fault tolerance and balancedness. We empirically compare our approach with baselines based on random partitioning, balanced partition trees, and locality sensitive hashing, showing that we achieve significantly higher accuracy on both synthetic and real world image and advertising datasets. We also demonstrate that our technique strongly scales with the available computing power.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    International Conference on Artificial Intelligence and Statistics

  • Publication date

    2015-12-01

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1512.04848
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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