Title: A Bridge Between Hyperparameter Optimization and Larning-to-learn,
Abstract: We consider a class of a nested optimization problems involving inner and
outer objectives. We observe that by taking into explicit account the
optimization dynamics for the inner objective it is possible to derive a
general framework that unifies gradient-based hyperparameter optimization and
meta-learning (or learning-to-learn). Depending on the specific setting, the
variables of the outer objective take either the meaning of hyperparameters in
a supervised learning problem or parameters of a meta-learner. We show that
some recently proposed methods in the latter setting can be instantiated in our
framework and tackled with the same gradient-based algorithms. Finally, we
discuss possible design patterns for learning-to-learn and present encouraging
preliminary experiments for few-shot learning.