Omar Montasser

According to our database1, Omar Montasser authored at least 16 papers between 2017 and 2024.

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Bibliography

2024
Agnostic Multi-Robust Learning using ERM.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Theoretical Foundations of Adversarially Robust Learning.
CoRR, 2023

Certifiable (Multi)Robustness Against Patch Attacks Using ERM.
CoRR, 2023

Strategic Classification under Unknown Personalized Manipulation.
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.
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.
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.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Transductive Robust Learning Guarantees.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Adversarially Robust Learning with Unknown Perturbation Sets.
Proceedings of the Conference on Learning Theory, 2021

2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning.
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.
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.
Proceedings of the Information Theory and Applications Workshop, 2020

Efficiently Learning Adversarially Robust Halfspaces with Noise.
Proceedings of the 37th International Conference on Machine Learning, 2020

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity.
Proceedings of the Conference on Learning Theory, 2020

2019
VC Classes are Adversarially Robustly Learnable, but Only Improperly.
Proceedings of the Conference on Learning Theory, 2019

2017
Predicting Demographics of High-Resolution Geographies with Geotagged Tweets.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017


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