Based on this bound, our detector continuously monitors the operation of the network over a test window and fires off an alarm whenever a deviation is detected.

Extensive experiments and analyses have been conducted to characterize and demonstrate the effectiveness of the proposed DivOE.

Previous IGF notions only focus on the direct utility of the item exposures, i.e., the exposure numbers across different item groups.

Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level.

Machine learning (ML) models are known to leak information about their training data.

We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model.

Machine learning models often fail on out-of-distribution data.

Receiver Operating Characteristics (ROC) is a popular tool to describe the trade-off between the True Positive Rate (TPR) and False Positive Rate (FPR) of a scoring function.