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Found 6 papers, 1 papers with code
Adversarial Consistency and the Uniqueness of the Adversarial Bayes Classifier
Adversarial training is a common technique for learning robust classifiers.
A Notion of Uniqueness for the Adversarial Bayes Classifier
We propose a new notion of uniqueness for the adversarial Bayes classifier in the setting of binary classification.
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks.
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification
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.
The Consistency of Adversarial Training for Binary Classification
Robustness to adversarial perturbations is of paramount concern in modern machine learning.
On the Existence of the Adversarial Bayes Classifier (Extended Version)
Our results can provide a useful tool for a subsequent study of surrogate losses in adversarial robustness and their consistency properties.
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