This post is cross-listed on the CMU ML blog.
The history of machine learning has largely been a story of increasing
abstraction. In the dawn of ML, researchers spent considerable effort
engineering features. As deep learning gained popularity, researchers then
shifted towards tuning the update rules and learning rates for their
optimizers. Recent research in meta-learning has climbed one level of
abstraction higher: many researchers now spend their days manually constructing
task distributions, from which they can automatically learn good optimizers.
What might be the next rung on this ladder? In this post we introduce theory
and algorithms for unsupervised meta-learning, where machine learning
algorithms themselves propose their own task distributions. Unsupervised
meta-learning further reduces the amount of human supervision required to solve
tasks, potentially inserting a new rung on this ladder of abstraction.