1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Adversarial training for tabular data with attack propagation
Tiago Leon Melo, João Bravo, Marco O. P. Sampaio +4
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying…
cs.LG2023★ 1 cited
Hyper-parameter Tuning for Adversarially Robust Models
Pedro Mendes, Paolo Romano, David Garlan
This work focuses on the problem of hyper-parameter tuning (HPT) for robust (i.e., adversarially trained) models, shedding light on the new challenges and opportunities arising dur…