  Conformal prediction (CP) can convert any model's output into prediction sets
guaranteed to include the true label with any user-specified probability.
However, same as the model itself, CP is vulnerable to adversarial test
examples (evasion) and perturbed calibration data (poisoning). We derive
provably robust sets by bounding the worst-case change in conformity scores.
Our tighter bounds lead to more efficient sets. We cover both continuous and
discrete (sparse) data and our guarantees work both for evasion and poisoning
attacks (on both features and labels).
