Adaptive Conformal Inference Under Distribution Shift
By Isaac Gibbs, E.J. Candes. Neural Information Processing Systems, 2021
508 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/445596c40dc421efe2354a340085b43181bea2be
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Citations in the corpus
- A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification (2021, background): “For example, [76] builds a conformal procedure robust to shifts of known f -divergence in the score function, and [77] forms prediction sets in a data stream where the distribution varies over time in an unknown fashion by constantly re-estimating the conformal quantile.”
- Conformal Risk Control (2022, background): “Recently there have been many extensions of the conformal algorithm, mainly targeting deviations from exchangeability (Tibshirani et al., 2019; Gibbs & Candes, 2021; Barber et al., 2022; Fannjiang et al., 2022) and improved conditional coverage (Barber et al., 2020; Romano et al., 2019; Guan, 2020;…”