| VC classes are adversarially robustly learnable, but only improperly O Montasser, S Hanneke, N Srebro Conference on Learning Theory (COLT) 2019, 2019 | 163 | 2019 |
| Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples S Goldwasser, AT Kalai, Y Kalai, O Montasser Advances in Neural Information Processing Systems (NeurIPS) 2020 33, 2020 | 45 | 2020 |
| Reducing Adversarially Robust Learning to Non-Robust PAC Learning O Montasser, S Hanneke, N Srebro Advances in Neural Information Processing Systems (NeurIPS) 2020 33, 2020 | 39 | 2020 |
| Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity P Kamath, O Montasser, N Srebro Conference on Learning Theory (COLT) 2020, 2020 | 38 | 2020 |
| Efficiently Learning Adversarially Robust Halfspaces with Noise O Montasser, S Goel, I Diakonikolas, N Srebro International Conference on Machine Learning (ICML) 2020, 2020 | 36 | 2020 |
| Adversarially Robust Learning with Unknown Perturbation Sets O Montasser, S Hanneke, N Srebro Conference on Learning Theory (COLT) 2021, 2021 | 28 | 2021 |
| A theory of PAC learnability under transformation invariances H Shao, O Montasser, A Blum Advances in Neural Information Processing Systems 35, 13989-14001, 2022 | 23 | 2022 |
| Adversarially robust learning: A generic minimax optimal learner and characterization O Montasser, S Hanneke, N Srebro Advances in Neural Information Processing Systems 35, 37458-37470, 2022 | 20 | 2022 |
| Transductive Robust Learning Guarantees O Montasser, S Hanneke, N Srebro International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 14 | 2021 |
| Predicting demographics of high-resolution geographies with geotagged tweets O Montasser, D Kifer AAAI Conference on Artificial Intelligence (AAAI) 2017, 2017 | 12 | 2017 |
| Strategic classification under unknown personalized manipulation H Shao, A Blum, O Montasser Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
| Boosting barely robust learners: A new perspective on adversarial robustness A Blum, O Montasser, G Shakhnarovich, H Zhang Advances in Neural Information Processing Systems 35, 1307-1319, 2022 | 2 | 2022 |
| Derandomizing Multi-Distribution Learning KG Larsen, O Montasser, N Zhivotovskiy arXiv preprint arXiv:2409.17567, 2024 | 1 | 2024 |
| Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization O Montasser, H Shao, E Abbe arXiv preprint arXiv:2410.23461, 2024 | | 2024 |
| Derandomizing Multi-Distribution Learning K Green Larsen, O Montasser, N Zhivotovskiy arXiv e-prints, arXiv: 2409.17567, 2024 | | 2024 |
| Agnostic Multi-Robust Learning Using ERM S Ahmadi, A Blum, O Montasser, KM Stangl International Conference on Artificial Intelligence and Statistics, 2242-2250, 2024 | | 2024 |
| Theoretical Foundations of Adversarially Robust Learning O Montasser arXiv preprint arXiv:2306.07723, 2023 | | 2023 |
| Certifiable (Multi) Robustness Against Patch Attacks Using ERM. S Ahmadi, A Blum, O Montasser, K Stangl CoRR, 2023 | | 2023 |
| Identifying unpredictable test examples with worst-case guarantees S Goldwasser, AT Kalai, YT Kalai, O Montasser 2020 Information Theory and Applications Workshop (ITA), 1-14, 2020 | | 2020 |
| Predicting Demographics of High-Resolution Geographies OT Montasser | | 2017 |
Nathan SrebroProfessor, TTIC and University of Chicago在 ttic.edu 的电子邮件经过验证
