A Notion of Uniqueness for the Adversarial Bayes Classifier
Abstract
Abstract. 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 all adversarial Bayes classifiers for a well-motivated family of one-dimensional data distributions. This characterization is then leveraged to show that as the perturbation radius increases, certain notions of regularity of adversarial Bayes classifiers improve. These results provide tools for understanding relationships between the Bayes and adversarial Bayes classifiers in one dimension.
Fields of Study & Subjects
Adversarial Robustness in Machine Learning
Privacy-Preserving Technologies in Data
Machine Learning and Data Classification
Publication details
- Journal / source
- SIAM Journal on Mathematics of Data Science
- Volume
- 8
- Issue
- 1
- Pages
- 77–107
- Publication date
- 11 February 2026
- Language
- EN