Optimistic concurrency control for distributed unsupervised learning

Xinghao Pan, Joseph Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan

Research output: Contribution to journalConference articlepeer-review

22 Scopus citations

Abstract

Research on distributed machine learning algorithms has focused primarily on one of two extremes-algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this "optimistic concurrency control" paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
StatePublished - 2013
Externally publishedYes
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: 5 Dec 201310 Dec 2013

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