| 2026 | COLT | DDPM Score Matching and Distribution Learning (Extended Abstract). | Sinho Chewi, Alkis Kalavasis, Anay Mehrotra, Omar Montasser |
| 2025 | COLT | Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks. | Omar Montasser, Abhishek Shetty, Nikita Zhivotovskiy |
| 2024 | AISTATS | Agnostic Multi-Robust Learning using ERM. | Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M. Stangl |
| 2022 | AISTATS | Transductive Robust Learning Guarantees. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2021 | COLT | Adversarially Robust Learning with Unknown Perturbation Sets. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2020 | COLT | Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. | Pritish Kamath, Omar Montasser, Nathan Srebro |
| 2020 | ICML | Efficiently Learning Adversarially Robust Halfspaces with Noise. | Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro |
| 2020 | ITA | Identifying unpredictable test examples with worst-case guarantees. | Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser |
| 2019 | COLT | VC Classes are Adversarially Robustly Learnable, but Only Improperly. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2017 | AAAI | Predicting Demographics of High-Resolution Geographies with Geotagged Tweets. | Omar Montasser, Daniel Kifer |