Certifiable Robustness for Naive Bayes Classifiers

no code implementations • 8 Mar 2023 • Song Bian, Xiating Ouyang, Zhiwei Fan, Paraschos Koutris

We present (i) a linear time algorithm in the number of entries in the dataset that decides whether a test point is certifiably robust for NBC, (ii) an algorithm that counts for each label, the number of cleaned datasets on which the NBC can be trained to predict that label, and (iii) an efficient optimal algorithm that poisons a clean dataset by inserting the minimum number of missing values such that a test point is not certifiably robust for NBC.

Data Poisoning