Dimensionality Reduction via Program Induction

How can techniques drawn from machine learning be appliedto the learning of structured, compositional representations? In this work, we adopt functional programs as our representation, and cast the problem of learning symbolic representations as a symbolic analog of dimensionality reduction. By placing program synthesis within a probabilistic machinelearning framework, we are able to model the learning ofsome English inflectional morphology and solve a set of synthetic regression problems.