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Cited by 866

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Computational Advertising: Techniques for Targeting Relevant Ads

Open access
Woodruff, David P.·2014·Foundations and Trends® in Theoretical Computer Science

Abstract

This survey highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix, one first compresses it to a much smaller matrix by multiplying it by a (usually) random matrix with certain properties. Much of the expensive computation can then be performed on the smaller matrix, thereby accelerating the solution for the original problem. In this survey we consider least squares as well as robust regression problems, low rank approximation, and graph sparsification. We also discuss a number of variants of these problems. Finally, we discuss the limitations of sketching methods.

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Metadata

Authors
Woodruff, D. P.
Venue
Foundations and Trends® in Theoretical Computer Science
Publisher
Emerald
Published
2014
Volume
10
Issue
1-2
Pages
1-157
ISSN
  • 1551-305X (ISSN-L)
  • 1551-3068
OpenAlex
W2157988812
Cited by
866
References
61
FWCI
58.61
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Computational Advertising: Techniques for Targeting Relevant Ads. https://app.cochat.ai/papers/10.1561/0400000060
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Woodruff, D. P. (2014). Computational Advertising: Techniques for Targeting Relevant Ads. Foundations and Trends® in Theoretical Computer Science. https://doi.org/10.1561/0400000060

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.