  Many inductive logic programming approaches struggle to learn programs from
noisy data. To overcome this limitation, we introduce an approach that learns
minimal description length programs from noisy data, including recursive
programs. Our experiments on several domains, including drug design, game
playing, and program synthesis, show that our approach can outperform existing
approaches in terms of predictive accuracies and scale to moderate amounts of
noise.
