261 citations · 347 across the 12 of their papers we have counts for
4 papers · 1 filter
The Adversarial Consistency of Surrogate Risks for Binary Classification
Natalie Frank, Jonathan Niles-Weed
We study the consistency of surrogate risks for robust binary classification. It is common to learn robust classifiers by adversarial training, which seeks to minimize the expected…
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…
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…
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian +1
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed…