Robust and data-efficient blackbox optimization

Abstract

The present disclosure provides iterative blackbox optimization techniques that estimate the gradient of a function. According to an aspect of the present disclosure, a plurality of perturbations used at each iteration can be sampled from a non-orthogonal sampling distribution. As one example, in some implementations, perturbations that have been previously evaluated in previous iterations can be re-used at the current iteration, thereby conserving computing resources because the re-used perturbations do not need to be re-evaluated at the current iteration. In another example, in addition or alternatively to the use of previously evaluated perturbations, the perturbations evaluated at the current iteration can be sampled from a non-orthogonal sampling distribution.