Optimum statistical estimation with strategic data sources
Name
Cai15.pdf
Description
Published version
Size
321.01 KB
Format
Adobe PDF
Checksum (MD5)
1b6ec7142cf867edcf7fa62bc60465f7
Author(s)
Cai, Y
Daskalakis, C
Papadimitriou, C
Date Issued
January 1, 2015
Journal
Journal of Machine Learning Research
Citation
Cai, Y, Daskalakis, C and Papadimitriou, C. 2015. "Optimum statistical estimation with strategic data sources." Journal of Machine Learning Research, 40 (2015).
Version
Final published version
Abstract
© 2015 A. Agarwal & S. Agarwal. We propose an optimum mechanism for providing monetary incentives to the data sources of a statistical estimator such as linear regression, so that high quality data is provided at low cost, in the sense that the weighted sum of payments and estimation error is minimized. The mechanism applies to a broad range of estimators, including linear and polynomial regression, kernel regression, and, under some additional assumptions, ridge regression. It also generalizes to several objectives, including minimizing estimation error subject to budget constraints. Besides our concrete results for regression problems, we contribute a mechanism design framework through which to design and analyze statistical estimators whose examples are supplied by workers with cost for labeling said examples.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://proceedings.mlr.press/v40/Cai15.html
