Continually Adapting Optimizers Improve Meta-Generalization

Wenyi Wang, Louis Kirsch, Francesco Faccio, Mingchen Zhuge, Jürgen Schmidhuber

Keywords: Learned Optimization, Meta Learning, Adaptation, Generalization

TL;DR: We introduce a guard mechanism that allows learned optimizers to adapt to an unseen task at meta-test time.

Abstract: Meta-learned optimizers increasingly outperform analytical handcrafted optimizers such as SGD and Adam. On some tasks, however, they fail to generalize strongly, underperforming handcrafted methods. Then one can fall back on handcrafted methods through a guard, to combine the efficiency benefits of learned optimizers and the guarantees of analytical methods. At some point in the iterative optimization process, however, such guards may make the learned optimizer incompatible with the remaining optimization, and thus useless for further progress. Our novel method Meta Guard keeps adapting the learned optimizer to the target optimization problem. It experimentally outperforms other baselines, adapting to new tasks during training.

Submission Number: 109