Understanding Benign Overfitting in Gradient-Based Meta Learning
This work addresses a theoretical gap in understanding benign overfitting for researchers in meta learning and few-shot learning, but it is incremental as it builds on existing concepts with linear models.
The paper investigates why overparameterized meta learning models can generalize well despite statistical theory predicting overfitting, focusing on gradient-based meta linear regression and providing theoretical analysis and numerical simulations.
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.