3 papers
cs.LG2025
Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training…
cs.LG2024
Adversarial Consistency and the Uniqueness of the Adversarial Bayes Classifier
Natalie S. Frank
Minimizing an adversarial surrogate risk is a common technique for learning robust classifiers. Prior work showed that convex surrogate losses are not statistically consistent in t…
cs.LG2024
A Notion of Uniqueness for the Adversarial Bayes Classifier
Natalie S. Frank
We propose a new notion of uniqueness for the adversarial Bayes classifier in the setting of binary classification. Analyzing this concept produces a simple procedure for computing…