Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. - Pranjal Awasthi; Natalie Frank; Mehryar Mohri (ICML, 2020)

On the Rademacher Complexity of Linear Hypothesis Sets. - Pranjal Awasthi; Natalie Frank; Mehryar Mohri (CoRR, 2020)

On the Existence of The Adversarial Bayes Classifier. - Pranjal Awasthi; Natalie Frank; Mehryar Mohri (NeurIPS, 2021)

Calibration and Consistency of Adversarial Surrogate Losses. - Pranjal Awasthi; Natalie Frank; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2021)

On the Existence of the Adversarial Bayes Classifier (Extended Version). - Pranjal Awasthi; Natalie S. Frank; Mehryar Mohri (CoRR, 2021)

The Adversarial Consistency of Surrogate Risks for Binary Classification. - Natalie Frank; Jonathan Niles-Weed (NeurIPS, 2023)

Robust Explanations for Deep Neural Networks via Pseudo Neural Tangent Kernel Surrogate Models. - Andrew Engel; Zhichao Wang; Natalie S. Frank; Ioana Dumitriu; Sutanay Choudhury; Anand D. Sarwate; Tony Chiang (CoRR, 2023)

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models. - Andrew Engel; Zhichao Wang; Natalie Frank; Ioana Dumitriu; Sutanay Choudhury; Anand D. Sarwate; Tony Chiang (ICLR, 2024)

The Price of Implicit Bias in Adversarially Robust Generalization. - Nikolaos Tsilivis 002; Natalie Frank; Nati Srebro; Julia Kempe (NeurIPS, 2024)

A Notion of Uniqueness for the Adversarial Bayes Classifier. - Natalie S. Frank (CoRR, 2024)

Adversarial Consistency and the Uniqueness of the Adversarial Bayes Classifier. - Natalie S. Frank (CoRR, 2024)

Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification. - Natalie S. Frank; Jonathan Niles-Weed (J. Mach. Learn. Res., 2024)

Adversarial Surrogate Risk Bounds for Binary Classification. - Natalie S. Frank (CoRR, 2025)