Robust estimation is an important problem in statistics which aims at providing a reasonable estimator when the data-generating distribution lies within an appropriately defined ball around an uncontaminated distribution. Although minimax rates of estimation have been established in recent years, many existing robust estimators with provably optimal convergence rates are also computationally intractable. In this paper, we study several estimation problems under a Wasserstein contamination model and present computationally tractable estimators motivated by generative adversarial networks (GANs). Specifically, we analyze the properties of Wasserstein GAN-based estimators for location estimation, covariance matrix estimation and linear regression and show that our proposed estimators are minimax optimal in many scenarios. Finally, we present numerical results which demonstrate the effectiveness of our estimators.
- Award ID(s):
- 1856229
- PAR ID:
- 10536595
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- Journal Name:
- Network Science
- Volume:
- 11
- Issue:
- 3
- ISSN:
- 2050-1242
- Page Range / eLocation ID:
- 502 to 535
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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