The landscape of empirical risk for nonconvex losses

Annals of Statistics, pp. 2747-2774, 2018.

Cited by: 17|Bibtex|Views18|

Abstract:

Most high-dimensional estimation and prediction methods propose to minimize a cost function (empirical risk) that is written as a sum of losses associated to each data point. this paper we focus on the case of non-convex losses, which is practically important but still poorly understood. Classical empirical process theory implies uniform...More

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