Many decision problems cannot be solved exactly and use several estimation
algorithms that assign scores to the different available options. The
estimation errors can have various correlations, from low (e.g. between two
very different approaches) to high (e.g. when using a given algorithm with
different hyperparameters). Most aggregation rules would suffer from this
diversity of correlations. In this article, we propose different aggregation
rules that take correlations into account, and we compare them to naive rules
in various experiments based on synthetic data. Our results show that when
sufficient information is known about the correlations between errors, a
maximum likelihood aggregation should be preferred. Otherwise, typically with
limited training data, we recommend a method that we call Embedded Voting (EV).

通过考虑相关性，本文提出了不同的聚合规则，并将它们与朴素规则进行了比较。通过基于合成数据的各种实验证明，当关于误差之间的相关性已知足够信息时，最大似然聚合应该优先考虑。否则，通常情况下，建议使用一种称为嵌入式投票（EV）的方法。