  A core tension in models of concept learning is that the model must carefully
balance the tractability of inference against the expressivity of the
hypothesis class. Humans, however, can efficiently learn a broad range of
concepts. We introduce a model of inductive learning that seeks to be
human-like in that sense. It implements a Bayesian reasoning process where a
language model first proposes candidate hypotheses expressed in natural
language, which are then re-weighed by a prior and a likelihood. By estimating
the prior from human data, we can predict human judgments on learning problems
involving numbers and sets, spanning concepts that are generative,
discriminative, propositional, and higher-order.
