2025
Provably Safeguarding a Classifier from OOD and Adversarial Samples
ICLR 2025poster
This paper aims to transform a trained classifier into an abstaining classifier, such that the latter is provably protected from out-of-distribution and adversarial samples. The proposed Sample-efficient Probabilistic Detection using Extreme Value Theory (SPADE) approach relies on a Generalized Extr…