  Meta learning has demonstrated tremendous success in few-shot learning with
limited supervised data. In those settings, the meta model is usually
overparameterized. While the conventional statistical learning theory suggests
that overparameterized models tend to overfit, empirical evidence reveals that
overparameterized meta learning methods still work well -- a phenomenon often
called "benign overfitting." To understand this phenomenon, we focus on the
meta learning settings with a challenging bilevel structure that we term the
gradient-based meta learning, and analyze its generalization performance under
an overparameterized meta linear regression model. While our analysis uses the
relatively tractable linear models, our theory contributes to understanding the
delicate interplay among data heterogeneity, model adaptation and benign
overfitting in gradient-based meta learning tasks. We corroborate our
theoretical claims through numerical simulations.
