论文标题
对Stein的无偏风险估算的可访问评估，并使用凸正则估计
Tractable Evaluation of Stein's Unbiased Risk Estimate with Convex Regularizers
论文作者
论文摘要
Stein的无偏风险估计（当然）对$ \ ell_2 $估计的任何估计器的均值估算值进行了公正的估计。我们将重点放在估计器最小化二次损耗项和凸正则器的情况下。对于这些估计器，可以在一些特殊情况下进行分析评估，并且通常使用最近开发的通用方法来通过凸优化问题进行区分；但是，这些通用方法并未扩展到大问题。在本文中，我们描述了评估确保处理大量估计器的方法，并扩展到大型问题大小。
Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the $\ell_2$ risk of any estimator of the mean of a Gaussian random vector. We focus here on the case when the estimator minimizes a quadratic loss term plus a convex regularizer. For these estimators SURE can be evaluated analytically for a few special cases, and generically using recently developed general purpose methods for differentiating through convex optimization problems; these generic methods however do not scale to large problems. In this paper we describe methods for evaluating SURE that handle a wide class of estimators, and also scale to large problem sizes.
