---
res:
  bibo_abstract:
  - "The area of machine learning has made considerable progress over the past decade,
    enabled by the widespread availability of large datasets, as well as by improved
    algorithms and models. Given the large computational demands of machine learning
    workloads, parallelism, implemented either through single-node concurrency or
    through multi-node distribution, has been a third key ingredient to advances in
    machine learning.\r\nThe goal of this tutorial is to provide the audience with
    an overview of standard distribution techniques in machine learning, with an eye
    towards the intriguing trade-offs between synchronization and communication costs
    of distributed machine learning algorithms, on the one hand, and their convergence,
    on the other.The tutorial will focus on parallelization strategies for the fundamental
    stochastic gradient descent (SGD) algorithm, which is a key tool when training
    machine learning models, from classical instances such as linear regression, to
    state-of-the-art neural network architectures.\r\nThe tutorial will describe the
    guarantees provided by this algorithm in the sequential case, and then move on
    to cover both shared-memory and message-passing parallelization strategies, together
    with the guarantees they provide, and corresponding trade-offs. The presentation
    will conclude with a broad overview of ongoing research in distributed and concurrent
    machine learning. The tutorial will assume no prior knowledge beyond familiarity
    with basic concepts in algebra and analysis.\r\n@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
  bibo_doi: 10.1145/3212734.3212798
  dct_date: 2018^xs_gYear
  dct_identifier:
  - UT:000458186900063
  dct_isPartOf:
  - http://id.crossref.org/issn/9781450357951
  dct_language: eng
  dct_publisher: ACM@
  dct_title: A brief tutorial on distributed and concurrent machine learning@
...