Title: MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. Abstract: Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications. Standard techniques for handling class-imbalance usually work by training on a re-weighted loss or on re-balanced data.  Unfortunately, training overparameterized neural networks on such objectives causes rapid memorization of minority class data. To avoid this trap, we harness meta-learning, which uses both an "outer-loop'' and an "inner-loop'' loss, each of which may be balanced using different strategies. We evaluate our method, MetaBalance, on image classification, credit-card fraud detection, loan default prediction, and facial recognition tasks with severely imbalanced data. We find that MetaBalance outperforms a wide array of popular strategies designed to handle class-imbalance, especially in scenarios with very few samples in minority classes.