Continual learning aims to enable machine learning models to continually
learn from a shifting data distribution without forgetting what has already
been learned. Such shifting distributions can be broken into disjoint subsets
of related examples; by training each member of an ensemble on a different
subset it is possible for the ensemble as a whole to achieve much higher
accuracy with less forgetting than a naive model. We address the problem of
selecting which models within an ensemble should learn on any given data, and
which should predict. By drawing on work from delegative voting we develop an
algorithm for using delegation to dynamically select which models in an
ensemble are active. We explore a variety of delegation methods and performance
metrics, ultimately finding that delegation is able to provide a significant
performance boost over naive learning in the face of distribution shifts.

连续学习旨在使机器学习模型能够在数据分布不断变化的情况下不忘记已学知识。通过训练集合中的每个成员于不同相关子集，使整个集合相比于单纯模型能够获得更高准确率且遗忘更少的问题。我们解决了在给定数据上选择哪些模型应该学习，哪些模型应该进行预测的问题。通过借鉴委托投票的工作，我们开发了一种用于动态选择集合中活动模型的委托算法。我们探讨了各种委托方法和性能指标，最终发现在面对分布变化时，委托能够显著提高性能，优于单纯学习。