End-to-End Optimization and Learning for Multiagent Ensembles

James Kotary, Vincenzo Di Vito, Ferdinando Fioretto

Research output: Contribution to journalConference Articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2613-2615
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
StatePublished - 2023
Event22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom
Duration: May 29 2023Jun 2 2023


  • Ensemble multi-agent learning
  • decision focused learning
  • integration of optimization
  • learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering


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