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Super-sampling with a reservoir

Abstract:

We introduce an alternative to reservoir sampling, a classic and popular algorithm for drawing a fixed-size subsample from streaming data in a single pass. Rather than draw a random sample, our approach performs an online optimization which aims to select the subset that provides the best overall approximation to the full data set, as judged using a kernel two-sample test. This produces subsets which minimize the worst-case relative error when computing expectations of functions in a specifie...

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Publication status:
Accepted
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Publisher:
AUAI Press Publisher's website
Pages:
567-576
Publication date:
2016
ISSN:
1525-3384
URN:
uuid:62cf0b18-df18-4729-bd1f-21a9e834e2b9
Source identifiers:
625119
Local pid:
pubs:625119
ISBN:
978-0-9966431-1-5

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