Conformal prediction is a popular, modern technique for providing valid
predictive inference for arbitrary machine learning models. Its validity relies
on the assumptions of exchangeability of the data, and symmetry of the given
model fitting algorithm as a function of the data. However, exchangeability is
often violated when predictive models are deployed in practice. For example, if
the data distribution drifts over time, then the data points are no longer
exchangeable; moreover, in such settings, we might want to use a nonsymmetric
algorithm that treats recent observations as more relevant. This paper
generalizes conformal prediction to deal with both aspects: we employ weighted
quantiles to introduce robustness against distribution drift, and design a new
randomization technique to allow for algorithms that do not treat data points
symmetrically. Our new methods are provably robust, with substantially less
loss of coverage when exchangeability is violated due to distribution drift or
other challenging features of real data, while also achieving the same coverage
guarantees as existing conformal prediction methods if the data points are in
fact exchangeable. We demonstrate the practical utility of these new tools with
simulations and real-data experiments on electricity and election forecasting.

本文提出一种新的针对预测模型的 Conformal prediction 泛化方法，通过引入加权分位数来抵御数据分布漂移的影响，同时设计一种新的随机化技术，允许不对称处理数据点的算法。实验表明，该方法在数据点不可交换的情况下具有较高的鲁棒性，并且在数据点可交换情况下达到了与现有方法相同的覆盖率保证。