Time–Data Tradeoffs by Aggressive Smoothing

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.


Presented at:
Conference of Neural Information Processing Systems (NIPS) Foundation 2014, Montreal, Quebec, Canada, December 8-11, 2014
Year:
2014
Laboratories:




 Record created 2014-10-30, last modified 2018-01-28

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