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In-database learning with sparse tensors

Abstract:

In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This paper introduces a unified framework for training and evaluating a class of stat...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
  • (Accepted manuscript, pdf, 690.5KB)
Publisher copy:
10.1145/3196959.3196960

Authors


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Institution:
University of Oxford
Oxford college:
St Cross College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
Association for Computing Machinery Publisher's website
Journal:
ACM Principles of Database Systems Journal website
Pages:
325-340
Host title:
SIGMOD/PODS '18 Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
Publication date:
2018-05-27
Acceptance date:
2017-09-01
Event location:
Houston
DOI:
Source identifiers:
725630
ISBN:
9781450347068
Keywords:
Pubs id:
pubs:725630
UUID:
uuid:2d852e0d-889d-46fe-890e-b1ac5687c798
Local pid:
pubs:725630
Deposit date:
2017-09-05

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