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Conference item

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

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Funding agency for:
Wood, F
Grant:
DARPA PPAML 61160290-111668
Publisher:
AUAI Press Publisher's website
Journal:
Uncertainty in artificial intelligence : proceedings of the conference Conference on Uncertainty in Artificial Intelligence Journal website
Pages:
567-576
Host title:
UAI'16 Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence
Publication date:
2016-01-01
Acceptance date:
2016-05-06
ISSN:
1525-3384
Source identifiers:
625119
ISBN:
9780996643115
Pubs id:
pubs:625119
UUID:
uuid:62cf0b18-df18-4729-bd1f-21a9e834e2b9
Local pid:
pubs:625119
Deposit date:
2016-06-02

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