ICML 2021
Workshop paper

Nonconvex Min-Max Bilevel Optimization for Task Robust Meta Learning

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As the number of learning tasks increases, robustness and adaptation both become the most important criteria for measuring the performance of modern machine learning models. This paper mainly focuses on developing a generic robust bilevel optimization framework with possible applications to meta-learning, transfer learning and continual learning. By leveraging the recent advances of nonconvex minmax optimization, our proposed gradient descent and ascent bilevel optimization (TaRo-BOBA) algorithm is able to extract a task robust latent space to overcome the distributional shift between the training and meta testing data sets. Theoretical analyses show that TaRo-BOBA converges to the first-order stationary point in a rate of $O(\sqrt{n}K^{−2/5})$, where $K$ denotes the total number of iterations and $n$ denotes the number of tasks. To the best of our knowledge, this is the first work that formulates the task robust meta-learning as a minmax bilevel optimization problem and provides a single loop gradient-based algorithm with provable convergence rate guarantees.


24 Jul 2021


ICML 2021