In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial ...

Dec 5, 2021 · In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings.

Our results show that resilience verification is useful and feasible in practice, yielding a more reliable security assessment of both standard and robust ...

In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in ...

2022. CoSe. Beyond robustness: Resilience verification of tree-based classifiers. Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, and 2 more authors.

Beyond Robustness: Resilience Verification of Tree-Based Classifiers ... In this paper we criticize the robustness measure traditionally employed to assess the ...

To mitigate the limitations of robustness, we introduce a new measure called resilience and we focus on its verification. In particular, we discuss how ...

Beyond robustness: Resilience verification of tree-based classifiers. S Calzavara, L Cazzaro, C Lucchese, F Marcuzzi, S Orlando. Computers & Security 121 ...

Mar 20, 2024 · We study the robustness verification problem for tree based models, including decision trees, random forests (RFs) and gradient boosted decision ...

In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings.