Adaptive Conformal Inference Under Distribution Shift
This addresses the challenge of maintaining reliable prediction intervals for users in dynamic environments where data distributions change over time, representing a novel extension of conformal inference beyond exchangeability assumptions.
The paper tackles the problem of forming prediction sets in an online setting with unknown distribution shifts, and the result is a method that provably achieves desired coverage frequency over long intervals, as demonstrated on real-world datasets.
We develop methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion. Our framework builds on ideas from conformal inference to provide a general wrapper that can be combined with any black box method that produces point predictions of the unseen label or estimated quantiles of its distribution. While previous conformal inference methods rely on the assumption that the data points are exchangeable, our adaptive approach provably achieves the desired coverage frequency over long-time intervals irrespective of the true data generating process. We accomplish this by modelling the distribution shift as a learning problem in a single parameter whose optimal value is varying over time and must be continuously re-estimated. We test our method, adaptive conformal inference, on two real world datasets and find that its predictions are robust to visible and significant distribution shifts.