Xiating Ouyang

Naive Bayes Classifiers over Missing Data: Decision and Poisoning

We study the certifiable robustness of ML classifiers on dirty datasets that could contain missing values. A test point is certifiably robust for an ML classifier if the classifier returns the same prediction for that test point, regardless of which cleaned version (among exponentially many) of the…