Conformal Prediction via Bayesian Quadrature

This paper explores a novel perspective on conformal prediction, a method for providing performance guarantees for machine learning models without assuming a specific data distribution. The authors propose viewing conformal prediction through a Bayesian lens, specifically utilizing Bayesian quadrature, a technique for estimating integrals with uncertainty. They argue that this approach addresses limitations of traditional frequentist-based conformal prediction, offering more interpretable guarantees and a richer understanding of potential future losses. The paper demonstrates how existing techniques like split conformal prediction and conformal risk control can be understood as special cases of their Bayesian framework. Ultimately, the authors show that their method, grounded in Bayesian probability, can provide a more nuanced and robust way to quantify uncertainty for complex models.