Multiagent ensemble learning is an important class of algorithms aimed at creating accurate and robust 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 addresses this challenge and proposes a unique integration of constrained optimization and learning to derive specialized consensus rules to compose accurate predictions from a pretrained ensemble. The resulting strategy, called end-to-end Multiagent ensemble Learning (e2e-MEL), learns to select appropriate predictors to combine for a particular input sample. The paper shows how to derive the ensemble learning task into a differentiable selection program which is trained end-to-end within the ensemble learning model. Results over standard benchmarks demonstrate the ability of e2e-MEL to substantially outperform conventional consensus rules in a variety of settings.
翻译:多试剂共同学习是一系列重要的算法,目的是通过综合个别物剂的预测,建立准确和健全的机器学习模型。这些模型设计的一个关键挑战是如何制定有效的规则,将任何特定投入样本的个别预测结合起来。本文件讨论这一挑战,并提议将限制优化和学习的独特整合,以形成专门的共识规则,从预先培训的组合中得出准确的预测。由此产生的战略称为端对端多试剂共同学习(e2e-MEL),学会选择适当的预测器,以结合特定输入样本。论文展示了如何将共同学习任务纳入一个不同的选择方案,该选择方案是在共同学习模型中经过培训的端对端。超过标准基准的结果表明e-MEL有能力在各种环境下大大超越常规的共识规则。