Communication-efficient distributed online learning with kernels

Michael Kamp, Sebastian Bothe, Mario Boley, Michael Mock

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms—including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2016 Riva del Garda, Italy, September 19–23, 2016 Proceedings, Part II
EditorsPaolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Giuseppe
Place of PublicationCham Switzerland
Number of pages15
ISBN (Electronic)9783319462271
ISBN (Print)9783319462264
Publication statusPublished - 2016
Externally publishedYes
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases 2016 - Riva del Garda, Italy
Duration: 19 Sep 201623 Sep 2016
Conference number: 15th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases 2016
Abbreviated titleECML PKDD 2016
CityRiva del Garda
Internet address

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