When Does MAML Objective Have Benign Landscape?
This work addresses the theoretical understanding of MAML's convergence properties for researchers in meta-learning and optimization, but it appears incremental as it builds on existing MAML analysis without introducing new methods or broad applications.
The paper investigates the optimization landscape of the Model-Agnostic Meta-Learning (MAML) algorithm, specifically analyzing when it has a benign landscape that enables global convergence to optimal solutions, using Linear Quadratic Regulator (LQR) tasks as an example to identify structural similarities that facilitate this.
The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm. The goal of the study is to determine the global convergence of MAML on sequential decision-making tasks possessing a common structure. We are curious to know when, if at all, the benign landscape of the underlying tasks results in a benign landscape of the corresponding MAML objective. For illustration, we analyze the landscape of the MAML objective on LQR tasks to determine what types of similarities in their structures enable the algorithm to converge to the globally optimal solution.