  Gradient boosted decision trees have achieved remarkable success in several
domains, particularly those that work with static tabular data. However, the
application of gradient boosted models to signal processing is underexplored.
In this work, we introduce gradient boosted filters for dynamic data, by
employing Hammerstein systems in place of decision trees. We discuss the
relationship of our approach to the Volterra series, providing the theoretical
underpinning for its application. We demonstrate the effective generalizability
of our approach with examples.
