  Knowledge graph embeddings rank among the most successful methods for link
prediction in knowledge graphs, i.e., the task of completing an incomplete
collection of relational facts. A downside of these models is their strong
sensitivity to model hyperparameters, in particular regularizers, which have to
be extensively tuned to reach good performance [Kadlec et al., 2017]. We
propose an efficient method for large scale hyperparameter tuning by
interpreting these models in a probabilistic framework. After a model
augmentation that introduces per-entity hyperparameters, we use a variational
expectation-maximization approach to tune thousands of such hyperparameters
with minimal additional cost. Our approach is agnostic to details of the model
and results in a new state of the art in link prediction on standard benchmark
data.

Machine Learning;Artificial Intelligence;Machine Learning