Adversarial robustness is an increasingly critical property of classifiers in applications. The design of robust algorithms relies on surrogate losses since the optimization of the adversarial loss with most hypothesis sets is NP-hard.

The whole is greater than its parts and the parts constitute the whole.

The sliding window model of computation captures scenarios in which data is arriving continuously,butonly thelatestwelements should beused foranalysis.

Noisy labels are inevitable in large real-world datasets.

Therefore, itmay be necessary to impose aseparate constraint for each such intersection.