  A semi-parametric, non-linear regression model in the presence of latent
variables is applied towards learning network graph structure. These latent
variables can correspond to unmodeled phenomena or unmeasured agents in a
complex system of interacting entities. This formulation jointly estimates
non-linearities in the underlying data generation, the direct interactions
between measured entities, and the indirect effects of unmeasured processes on
the observed data. The learning is posed as regularized empirical risk
minimization. Details of the algorithm for learning the model are outlined.
Experiments demonstrate the performance of the learned model on real data.
