Model inference, such as model comparison, model checking, and model
selection, is an important part of model development. Leave-one-out
cross-validation (LOO) is a general approach for assessing the generalizability
of a model, but unfortunately, LOO does not scale well to large datasets. We
propose a combination of using approximate inference techniques and
probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation
for large datasets. We provide both theoretical and empirical results showing
good properties for large data.Comment: Accepted to ICML 2019. This version is the submitted pape