  Few-shot meta-learning methods consider the problem of learning new tasks
from a small, fixed number of examples, by meta-learning across static data
from a set of previous tasks. However, in many real world settings, it is more
natural to view the problem as one of minimizing the total amount of
supervision --- both the number of examples needed to learn a new task and the
amount of data needed for meta-learning. Such a formulation can be studied in a
sequential learning setting, where tasks are presented in sequence. When
studying meta-learning in this online setting, a critical question arises: can
meta-learning improve over the sample complexity and regret of standard
empirical risk minimization methods, when considering both meta-training and
adaptation together? The answer is particularly non-obvious for meta-learning
algorithms with complex bi-level optimizations that may demand large amounts of
meta-training data. To answer this question, we extend previous meta-learning
algorithms to handle the variable-shot settings that naturally arise in
sequential learning: from many-shot learning at the start, to zero-shot
learning towards the end. On sequential learning problems, we find that
meta-learning solves the full task set with fewer overall labels and achieves
greater cumulative performance, compared to standard supervised methods. These
results suggest that meta-learning is an important ingredient for building
learning systems that continuously learn and improve over a sequence of
problems.
