  We present a "learning to learn" approach for automatically constructing
white-box classification loss functions that are robust to label noise in the
training data. We parameterize a flexible family of loss functions using Taylor
polynomials, and apply evolutionary strategies to search for noise-robust
losses in this space. To learn re-usable loss functions that can apply to new
tasks, our fitness function scores their performance in aggregate across a
range of training dataset and architecture combinations. The resulting
white-box loss provides a simple and fast "plug-and-play" module that enables
effective noise-robust learning in diverse downstream tasks, without requiring
a special training procedure or network architecture. The efficacy of our
method is demonstrated on a variety of datasets with both synthetic and real
label noise, where we compare favourably to previous work.
