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The Adversarial Consistency of Surrogate Risks for Binary Classification
We study the consistency of surrogate risks for robust binary classification.
On the Existence of The Adversarial Bayes Classifier
Adversarial robustness is a critical property in a variety of modern machine learning applications.
Calibration and Consistency of Adversarial Surrogate Losses
We then give a characterization of H-calibration and prove that some surrogate losses are indeed H-calibrated for the adversarial loss, with these hypothesis sets.
On the Rademacher Complexity of Linear Hypothesis Sets
Linear predictors form a rich class of hypotheses used in a variety of learning algorithms.
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
We give upper and lower bounds for the adversarial empirical Rademacher complexity of linear hypotheses with adversarial perturbations measured in $l_r$-norm for an arbitrary $r \geq 1$.
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