Natalie Frank

Jonathan Niles-Weed

J. Mach. Learn. Res., 2024

The Price of Implicit Bias in Adversarially Robust Generalization.

[BibT_eX]

[DOI]

CoRR, 2024

Adversarial Consistency and the Uniqueness of the Adversarial Bayes Classifier.

[BibT_eX]

[DOI]

CoRR, 2024

A Notion of Uniqueness for the Adversarial Bayes Classifier.

[BibT_eX]

[DOI]

CoRR, 2024

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Robust Explanations for Deep Neural Networks via Pseudo Neural Tangent Kernel Surrogate Models.

[BibT_eX]

[DOI]

CoRR, 2023

The Adversarial Consistency of Surrogate Risks for Binary Classification.

[BibT_eX]

[DOI]

Jonathan Niles-Weed

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification.

[BibT_eX]

[DOI]

CoRR, 2022

2021

On the Existence of the Adversarial Bayes Classifier (Extended Version).

[BibT_eX]

[DOI]

CoRR, 2021

Calibration and Consistency of Adversarial Surrogate Losses.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Existence of The Adversarial Bayes Classifier.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

On the Rademacher Complexity of Linear Hypothesis Sets.

[BibT_eX]

[DOI]

CoRR, 2020

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks.

[BibT_eX]

[DOI]