Adaptive Conformal Inference by Betting
This addresses the need for reliable uncertainty quantification in non-exchangeable real-world data, offering a more robust alternative to existing methods that require sensitive learning rate tuning.
The paper tackles the problem of adaptive conformal inference without exchangeability assumptions by proposing a parameter-free online convex optimization method, achieving long-term miscoverage control at a nominal level and demonstrating empirical performance without parameter tuning.
Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In this paper, we consider the problem of adaptive conformal inference without any assumptions about the data generating process. Existing approaches for adaptive conformal inference are based on optimizing the pinball loss using variants of online gradient descent. A notable shortcoming of such approaches is in their explicit dependence on and sensitivity to the choice of the learning rates. In this paper, we propose a different approach for adaptive conformal inference that leverages parameter-free online convex optimization techniques. We prove that our method controls long-term miscoverage frequency at a nominal level and demonstrate its convincing empirical performance without any need of performing cumbersome parameter tuning.