Efficient error and variance estimation for randomized matrix computations

arXiv:2207.06342v4 Announce Type: replace-cross
Abstract: Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid …

abstract algorithms applications arxiv become computing cs.na diagnostics error leave-one-out machine machine learning math.na matrix paper posterior quality stat.ml tools type variance