35 citations · 160 across the 33 of their papers we have counts for
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cs.LG2023
Naive Bayes Classifiers over Missing Data: Decision and Poisoning
Song Bian, Xiating Ouyang, Zhiwei Fan +1
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…
cs.LG2022
Certifiable Robustness for Nearest Neighbor Classifiers
Austen Z. Fan, Paraschos Koutris
ML models are typically trained using large datasets of high quality. However, training datasets often contain inconsistent or incomplete data. To tackle this issue, one solution i…