In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the distribution of the underlying data, except that the training and test data are assumed to be exchangeable. However, these methods bear a heavy computational cost-and, to be carried out exactly, the regression algorithm would need to be fitted infinitely many times. In practice, the conformal prediction method is run by simply considering only a finite grid of finely spaced values for the response variable. This paper develops discretized conformal prediction algorithms that are guaranteed to cover the target value with the desired probability, and that offer a tradeoff between computational cost and prediction accuracy.

翻译：在无已知真实底层模型的回归问题中，保形预测方法能够在不对底层数据分布作任何假设（仅假设训练数据和测试数据可交换）的前提下构建预测区间。然而，这些方法计算成本高昂——若要精确执行，回归算法需要被拟合无穷多次。实际应用中，保形预测方法仅通过考虑响应变量有限个精细划分的网格值来运行。本文提出了离散化保形预测算法，该算法能够以期望概率保证覆盖目标值，并在计算成本与预测精度之间提供可权衡的解决方案。