Model selection is a strategy aimed at creating accurate and robust models. A key challenge in designing these algorithms is identifying the optimal model for classifying any particular input sample. This paper addresses this challenge and proposes a novel framework for differentiable model selection integrating machine learning and combinatorial optimization. The framework is tailored for ensemble learning, a strategy that combines the outputs of individually pre-trained models, and learns to select appropriate ensemble members for a particular input sample by transforming the ensemble learning task into a differentiable selection program trained end-to-end within the ensemble learning model. Tested on various tasks, the proposed framework demonstrates its versatility and effectiveness, outperforming conventional and advanced consensus rules across a variety of settings and learning tasks.

翻译：模型选择是一种旨在构建准确且稳健模型的策略。设计此类算法的关键挑战在于为特定输入样本识别最优分类模型。本文针对这一挑战，提出了一种融合机器学习与组合优化的可微模型选择框架。该框架专为集成学习设计——即结合独立预训练模型输出的策略——通过将集成学习任务转化为可在集成学习模型内部进行端到端训练的可微选择程序，学习为特定输入样本选择合适的集成成员。在多种任务上的测试表明，所提出的框架展现了其通用性与有效性，在各类设定和学习任务中均优于传统及先进的共识规则。