Computationally efficient versions of conformal predictive distributions
This work addresses computational bottlenecks for researchers and practitioners using conformal methods in decision-making problems, representing an incremental improvement.
The paper tackles the computational inefficiency of conformal predictive systems in regression by proposing two efficient versions: split and cross-conformal predictive systems, with split offering guaranteed validity and cross providing greater predictive efficiency.
Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conformal predictive systems inherit the relative computational inefficiency of conformal predictors. In this paper we discuss two computationally efficient versions of conformal predictive systems, which we call split conformal predictive systems and cross-conformal predictive systems. The main advantage of split conformal predictive systems is their guaranteed validity, whereas for cross-conformal predictive systems validity only holds empirically and in the absence of excessive randomization. The main advantage of cross-conformal predictive systems is their greater predictive efficiency.