Technical Program

Paper Detail

Paper IDL.3.4
Paper Title Reliable Distributed Clustering with Redundant Data Assignment
Authors Venkata Gandikota, Arya Mazumdar, University of Massachusetts Amherst, United States; Ankit Singh Rawat, Google Research, United States
Session L.3: Distributed Learning Performance Analysis
Presentation Lecture
Track Statistics and Learning Theory
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract In this paper, we present distributed generalized clustering algorithms that can handle large scale data across multiple machines in spite of straggling or unreliable machines. We propose a novel data assignment scheme that enables us to obtain global information about the entire data even when some machines fail to respond with the results of the assigned local computations. The assignment scheme leads to distributed algorithms with good approximation guarantees for a variety of clustering and dimensionality reduction problems.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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