  The goal of item response theoretic (IRT) models is to provide estimates of
latent traits from binary observed indicators and at the same time to learn the
item response functions (IRFs) that map from latent trait to observed response.
However, in many cases observed behavior can deviate significantly from the
parametric assumptions of traditional IRT models. Nonparametric IRT models
overcome these challenges by relaxing assumptions about the form of the IRFs,
but standard tools are unable to simultaneously estimate flexible IRFs and
recover ability estimates for respondents. We propose a Bayesian nonparametric
model that solves this problem by placing Gaussian process priors on the latent
functions defining the IRFs. This allows us to simultaneously relax assumptions
about the shape of the IRFs while preserving the ability to estimate latent
traits. This in turn allows us to easily extend the model to further tasks such
as active learning. GPIRT therefore provides a simple and intuitive solution to
several longstanding problems in the IRT literature.
