Predicting Demographics of High-Resolution Geographies with Geotagged Tweets. - Omar Montasser; Daniel Kifer (AAAI, 2017)

VC Classes are Adversarially Robustly Learnable, but Only Improperly. - Omar Montasser; Steve Hanneke; Nathan Srebro (COLT, 2019)

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. - Pritish Kamath; Omar Montasser; Nathan Srebro (COLT, 2020)

Efficiently Learning Adversarially Robust Halfspaces with Noise. - Omar Montasser; Surbhi Goel; Ilias Diakonikolas; Nathan Srebro (ICML, 2020)

Identifying unpredictable test examples with worst-case guarantees. - Shafi Goldwasser; Adam Tauman Kalai; Yael Tauman Kalai; Omar Montasser (ITA, 2020)

Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. - Shafi Goldwasser; Adam Tauman Kalai; Yael Kalai; Omar Montasser (NeurIPS, 2020)

Reducing Adversarially Robust Learning to Non-Robust PAC Learning. - Omar Montasser; Steve Hanneke; Nati Srebro (NeurIPS, 2020)

Adversarially Robust Learning with Unknown Perturbation Sets. - Omar Montasser; Steve Hanneke; Nathan Srebro (COLT, 2021)

Transductive Robust Learning Guarantees. - Omar Montasser; Steve Hanneke; Nathan Srebro (AISTATS, 2022)

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. - Avrim Blum; Omar Montasser; Greg Shakhnarovich; Hongyang Zhang 001 (NeurIPS, 2022)

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. - Omar Montasser; Steve Hanneke; Nati Srebro (NeurIPS, 2022)

A Theory of PAC Learnability under Transformation Invariances. - Han Shao; Omar Montasser; Avrim Blum (NeurIPS, 2022)

Strategic Classification under Unknown Personalized Manipulation. - Han Shao; Avrim Blum; Omar Montasser (NeurIPS, 2023)

Certifiable (Multi)Robustness Against Patch Attacks Using ERM. - Saba Ahmadi; Avrim Blum; Omar Montasser; Kevin Stangl (CoRR, 2023)

Theoretical Foundations of Adversarially Robust Learning. - Omar Montasser (CoRR, 2023)

Agnostic Multi-Robust Learning using ERM. - Saba Ahmadi; Avrim Blum; Omar Montasser; Kevin M. Stangl (AISTATS, 2024)

Derandomizing Multi-Distribution Learning. - Kasper Green Larsen; Omar Montasser; Nikita Zhivotovskiy (NeurIPS, 2024)

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization. - Omar Montasser; Han Shao; Emmanuel Abbe (NeurIPS, 2024)

Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks. - Omar Montasser; Abhishek Shetty; Nikita Zhivotovskiy (COLT, 2025)

DDPM Score Matching and Distribution Learning. - Sinho Chewi; Alkis Kalavasis; Anay Mehrotra; Omar Montasser (CoRR, 2025)

(Im)possibility of Automated Hallucination Detection in Large Language Models. - Amin Karbasi; Omar Montasser; John Sous; Grigoris Velegkas (CoRR, 2025)

CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision. - Awni Altabaa; Omar Montasser; John Lafferty (CoRR, 2025)