---
res:
  bibo_abstract:
  - Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning,
    representing the optimization backbone for training several classic models, from
    regression to neural networks. Given the recent practical focus on distributed
    machine learning, significant work has been dedicated to the convergence properties
    of this algorithm under the inconsistent and noisy updates arising from execution
    in a distributed environment. However, surprisingly, the convergence properties
    of this classic algorithm in the standard shared-memory model are still not well-understood.
    In this work, we address this gap, and provide new convergence bounds for lock-free
    concurrent stochastic gradient descent, executing in the classic asynchronous
    shared memory model, against a strong adaptive adversary. Our results give improved
    upper and lower bounds on the "price of asynchrony'' when executing the fundamental
    SGD algorithm in a concurrent setting. They show that this classic optimization
    tool can converge faster and with a wider range of parameters than previously
    known under asynchronous iterations. At the same time, we exhibit a fundamental
    trade-off between the maximum delay in the system and the rate at which SGD can
    converge, which governs the set of parameters under which this algorithm can still
    work efficiently.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Dan-Adrian
      foaf_name: Alistarh, Dan-Adrian
      foaf_surname: Alistarh
      foaf_workInfoHomepage: http://www.librecat.org/personId=4A899BFC-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-3650-940X
  - foaf_Person:
      foaf_givenName: Christopher
      foaf_name: De Sa, Christopher
      foaf_surname: De Sa
  - foaf_Person:
      foaf_givenName: Nikola H
      foaf_name: Konstantinov, Nikola H
      foaf_surname: Konstantinov
      foaf_workInfoHomepage: http://www.librecat.org/personId=4B9D76E4-F248-11E8-B48F-1D18A9856A87
  bibo_doi: 10.1145/3212734.3212763
  dct_date: 2018^xs_gYear
  dct_identifier:
  - UT:000458186900022
  dct_isPartOf:
  - http://id.crossref.org/issn/9781450357951
  dct_language: eng
  dct_publisher: ACM@
  dct_title: The convergence of stochastic gradient descent in asynchronous shared
    memory@
...