In this paper, we study the generalization properties of Model-Agnostic Meta-Learning (MAML) algorithms for supervised learning problems. We focus on the setting in which we train the MAML model over m tasks, each with n data points, and characterize its generalization error from two points of view: First, we assume the new task at test time is one of the training tasks, and we show that, for strongly convex objective functions, the expected excess population loss is bounded by \mathcal{O}(1/mn) . Second, we consider the MAML algorithm's generalization to an unseen task and show that the resulting generalization error depends on the total variation distance between the underlying distributions of the new task and the tasks observed during the training process. Our proof techniques rely on the connections between algorithmic stability and generalization bounds of algorithms. In particular, we propose a new definition of stability for meta-learning algorithms, which allows us to capture the role of both the number of tasks m and number of samples per task n on the generalization error of MAML.

Based on a joint work with Aryan Mokhtari, UT Austin, and Asu Ozdaglar, MIT. Imagine sitting in your autonomous car, going for a vacation. Your vehicle should follow the directions provided by the navigation app, and also use multiple sensors to monitor other vehicles, road signs, street light, etc. As a result, during the course of your journey, your car might need to take actions within a few seconds, such as turning or stopping. The question is how should your vehicle be programmed to be able to adapt to the new tasks within a short amount of time and limited data.