  Conformal Predictors (CP) are wrappers around ML methods, providing error
guarantees under weak assumptions on the data distribution. They are suitable
for a wide range of problems, from classification and regression to anomaly
detection. Unfortunately, their high computational complexity limits their
applicability to large datasets.
  In this work, we show that it is possible to speed up a CP classifier
considerably, by studying it in conjunction with the underlying ML method, and
by exploiting incremental&decremental learning. For methods such as k-NN, KDE,
and kernel LS-SVM, our approach reduces the running time by one order of
magnitude, whilst producing exact solutions. With similar ideas, 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 CP for
regression.
  We evaluate our findings empirically, and discuss when methods are suitable
for CP optimization.
