  Bayesian nonparametric models, such as Gaussian processes, provide a
compelling framework for automatic statistical modelling: these models have a
high degree of flexibility, and automatically calibrated complexity. However,
automating human expertise remains elusive; for example, Gaussian processes
with standard kernels struggle on function extrapolation problems that are
trivial for human learners. In this paper, we create function extrapolation
problems and acquire human responses, and then design a kernel learning
framework to reverse engineer the inductive biases of human learners across a
set of behavioral experiments. We use the learned kernels to gain psychological
insights and to extrapolate in human-like ways that go beyond traditional
stationary and polynomial kernels. Finally, we investigate Occam's razor in
human and Gaussian process based function learning.
