Conformal prediction equips machine learning models with a reasonable notion of uncertainty quantification without making strong distributional assumptions. It wraps around any black-box prediction model and converts point predictions into set predictions that have a predefined marginal coverage guarantee. However, conformal prediction only works if we fix the underlying machine learning model in advance. A relatively unaddressed issue in conformal prediction is that of model selection and/or aggregation: for a given problem, which of the plethora of prediction methods (random forests, neural nets, regularized linear models, etc.) should we conformalize? This paper proposes a new approach towards conformal model aggregation in online settings that is based on combining the prediction sets from several algorithms by voting, where weights on the models are adapted over time based on past performance.

翻译：共形预测为机器学习模型赋予了合理的量化不确定性概念，而无需做出较强的分布假设。该方法可封装任意黑盒预测模型，将点预测转化为具有预设边际覆盖保证的集合预测。然而，共形预测仅在预先确定底层机器学习模型的前提下有效。共形预测中一个相对未被充分研究的问题是模型选择与/或聚合：对于特定问题，面对众多预测方法（随机森林、神经网络、正则化线性模型等），应选择哪种模型进行共形化？本文提出了一种面向在线场景的共形模型聚合新方法，该方法通过投票机制整合多个算法的预测集合，其中模型的权重会根据历史表现随时间动态调整。