Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification
problems, for a chosen significance level $\varepsilon $, CP guarantees that the error rate is
at most $\varepsilon $, irrespective of whether the underlying model is misspecified.
However, the prohibitive computational costs of" full" CP led researchers to design scalable
alternatives, which alas do not attain the same guarantees or statistical power of full CP. In …

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification
problems, a CP guarantees that the error rate is at most a chosen significance level,
irrespective of whether the underlying model is misspecified. However, the prohibitive
computational costs of full CP led researchers to design scalable alternatives, which alas do
not attain the same guarantees or statistical power of full CP. In this paper, we use influence …