  Covariance matrix estimation, a classical statistical topic, poses
significant challenges when the sample size is comparable to or smaller than
the number of features. In this paper, we frame covariance matrix estimation as
a compound decision problem and apply an optimal decision rule to estimate
covariance parameters. To approximate this rule, we introduce an algorithm that
integrates jackknife techniques with machine learning regression methods. This
algorithm exhibits adaptability across diverse scenarios without relying on
assumptions about data distribution. Simulation results and gene network
inference from an RNA-seq experiment in mice demonstrate that our approach
either matches or surpasses several state-of-the-art methods
