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conference paper not in proceedings
Time–Data Tradeoffs by Aggressive Smoothing
2014
This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization. As the amount of data increases, we can smooth optimization problems more and more aggressively to achieve accurate estimates more quickly. This work provides theoretical and experimental evidence of this tradeoff for a class of regularized linear inverse problems.
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Name
nips-paper 2014.pdf
Access type
openaccess
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363.25 KB
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Adobe PDF
Checksum (MD5)
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