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===Robust machine learning===
Robust machine learning typically refers to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is stable after adding some noise to the dataset.<ref>{{cite web|author=El Sayed Mahmoud |url=https://www.researchgate.net/post/What_is_the_definition_of_the_robustness_of_a_machine_learning_algorithm |title=What is the definition of the robustness of a machine learning algorithm? |access-date=2016-11-13}}</ref> Recently, consistently with their rise in popularity, there has been an increasing interest in the robustness of neural networks. This is particularly due their vulnerability to adverserial attacks. <ref>{{cite journal |last1=Li |first1=Linyi |last2=Xie |first2=Tao |last3=Li |first3=Bo |title=SoK: Certified Robustness for Deep Neural Networks |journal=arXiv:2009.04131 [cs, stat] |date=9 September 2022 |doi=https://doi.org/10.48550/arXiv.2009.04131 |url=https://arxiv.org/pdf/2009.04131.pdf}}</ref>
===Robust network design===
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