End-to-End Optimization and Learning for Multiagent Ensembles

Ensemble learning is an important class of algorithms aiming at creating accurate machine learning models by combining predictions from individual agents. A key challenge for the design of these models is to create effective rules to combine individual predictions for any particular input sample. This paper proposes a unique integration of constrained optimization and learning to derive specialized consensus rules. The paper shows how to derive the ensemble learning task as end-to-end training of a discrete subset selection module. Results over standard benchmarks demonstrate an ability to substantially outperform conventional consensus rules in a variety of settings.