Title: Searching for Robustness: Loss Learning for Noisy Classification Tasks. Abstract: 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 paramaterize 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 datasets 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.