Omar Montasser

According to our database, Omar Montasser authored at least 17 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization.

Omar Montasser, Han Shao, Emmanuel Abbe

CoRR, 2024

Agnostic Multi-Robust Learning using ERM.

Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M. Stangl

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Theoretical Foundations of Adversarially Robust Learning.

Omar Montasser

CoRR, 2023

Certifiable (Multi)Robustness Against Patch Attacks Using ERM.

Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl

CoRR, 2023

Strategic Classification under Unknown Personalized Manipulation.

Han Shao, Avrim Blum, Omar Montasser

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.

Han Shao, Omar Montasser, 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.

Omar Montasser, Steve Hanneke, 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.

Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang

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

Transductive Robust Learning Guarantees.

Omar Montasser, Steve Hanneke, Nathan Srebro

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

2021

Adversarially Robust Learning with Unknown Perturbation Sets.

Omar Montasser, Steve Hanneke, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2021

2020

Reducing Adversarially Robust Learning to Non-Robust PAC Learning.

Omar Montasser, Steve Hanneke, 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.

Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser

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.

Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser

Proceedings of the Information Theory and Applications Workshop, 2020

Efficiently Learning Adversarially Robust Halfspaces with Noise.

Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro

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

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

Pritish Kamath, Omar Montasser, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2020

2019

VC Classes are Adversarially Robustly Learnable, but Only Improperly.

Omar Montasser, Steve Hanneke, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2019

2017

Predicting Demographics of High-Resolution Geographies with Geotagged Tweets.

Omar Montasser, Daniel Kifer

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017