Omar Montasser

CoRR, 2023

Certifiable (Multi)Robustness Against Patch Attacks Using ERM.

[BibT_eX]

[DOI]

CoRR, 2023

Strategic Classification under Unknown Personalized Manipulation.

[BibT_eX]

[DOI]

Han Shao

Avrim Blum

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

2022

A Theory of PAC Learnability under Transformation Invariances.

[BibT_eX]

[DOI]

Han Shao

Avrim Blum

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization.

[BibT_eX]

[DOI]

Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Transductive Robust Learning Guarantees.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Adversarially Robust Learning with Unknown Perturbation Sets.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2021

2020

Reducing Adversarially Robust Learning to Non-Robust PAC Learning.

[BibT_eX]

[DOI]

Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Identifying unpredictable test examples with worst-case guarantees.

[BibT_eX]

[DOI]

Proceedings of the Information Theory and Applications Workshop, 2020

Efficiently Learning Adversarially Robust Halfspaces with Noise.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity.

[BibT_eX]

[DOI]

Pritish Kamath

Proceedings of the Conference on Learning Theory, 2020

2019

VC Classes are Adversarially Robustly Learnable, but Only Improperly.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2019

2017

Predicting Demographics of High-Resolution Geographies with Geotagged Tweets.

[BibT_eX]

[DOI]