6 citations · 20 across the 9 of their papers we have counts for
Showing 2022Show all
2 papers · 1 filter
cs.LG2022
The Consistency of Adversarial Training for Binary Classification
Natalie S. Frank, Jonathan Niles-Weed
Robustness to adversarial perturbations is of paramount concern in modern machine learning. One of the state-of-the-art methods for training robust classifiers is adversarial train…
cs.LG2022★ 3 cited
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification
Natalie S. Frank, Jonathan Niles-Weed
Adversarial training is one of the most popular methods for training methods robust to adversarial attacks, however, it is not well-understood from a theoretical perspective. We pr…