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Conformal Predictors (CP) are wrappers around ML models, providing errorguarantees under weak assumptions on the data distribution. They are suitablefor a wide range of problems, from classification and regression to anomalydetection. Unfortunately, their very high computational complexity limits theirapplicability to large datasets. In this work, we show that it is possible tospeed up a CP classifier considerably, by studying it in conjunction with theunderlying ML method, and by exploiting incremental&decremental learning. Formethods such as k-NN, KDE, and kernel LS-SVM, our approach reduces the runningtime by one order of magnitude, whilst producing exact solutions. With similarideas, we also achieve a linear speed up for the harder case of bootstrapping.Finally, we extend these techniques to improve upon an optimization of k-NN CPfor regression. We evaluate our findings empirically, and discuss when methodsare suitable for CP optimization.

Exact Optimization of Conformal Predictors via Incremental and Decremental Learning