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Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
[article]
2019
arXiv
pre-print
Robust scatter estimation is a fundamental task in statistics. The recent discovery on the connection between robust estimation and generative adversarial nets (GANs) by Gao et al. (2018) suggests that it is possible to compute depth-like robust estimators using similar techniques that optimize GANs. In this paper, we introduce a general learning via classification framework based on the notion of proper scoring rules. This framework allows us to understand both matrix depth function and
arXiv:1903.01944v1
fatcat:y7lcp4k7cjceda32fegosv466u