  Meta-learning is a promising strategy for learning to efficiently learn
within new tasks, using data gathered from a distribution of tasks. However,
the meta-learning literature thus far has focused on the task segmented
setting, where at train-time, offline data is assumed to be split according to
the underlying task, and at test-time, the algorithms are optimized to learn in
a single task. In this work, we enable the application of generic meta-learning
algorithms to settings where this task segmentation is unavailable, such as
continual online learning with a time-varying task. We present meta-learning
via online changepoint analysis (MOCA), an approach which augments a
meta-learning algorithm with a differentiable Bayesian changepoint detection
scheme. The framework allows both training and testing directly on time series
data without segmenting it into discrete tasks. We demonstrate the utility of
this approach on a nonlinear meta-regression benchmark as well as two
meta-image-classification benchmarks.
