no code implementations • NeurIPS 2023 • Natalie Frank, Jonathan Niles-Weed
We study the consistency of surrogate risks for robust binary classification.
no code implementations • NeurIPS 2021 • Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial robustness is a critical property in a variety of modern machine learning applications.
no code implementations • NeurIPS 2021 • Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong
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.
no code implementations • 21 Jul 2020 • Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Linear predictors form a rich class of hypotheses used in a variety of learning algorithms.
no code implementations • ICML 2020 • Pranjal Awasthi, Natalie Frank, Mehryar Mohri
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 $r \geq 1$.