2013/07/30 by Xinghao Pan, Joseph E. Gonzalez, Pan, Xinghao +7 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Optimization and Search Problems #Parallel #and Cluster Computing (cs.DC) #cs.AI #cs.DC #cs.LG
paper · pdf · doi:10.48550/arxiv.1307.8049
25 pages, 5 figures
arxiv created 2013/07/30 · openalex publication_date 2013/07/30 · arxiv updated 2013/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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.