Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization

von Montasser, Omar
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 48 of 50 (2023)

A Theory of PAC Learnability under Transformation Invariances

von Shao, Han
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 19 of 50 (2023)

REDUCING ADVERSARIALLY ROBUST LEARNING TO NON-ROBUST PAC LEARNING

von Montasser, Omar
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 18 of 27 (2021)

EFFICIENTLY LEARNING ADVERSARIALLY ROBUST HALFSPACES WITH NOISE

von Montasser, Omar
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 9 of 15 (2021)

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness

von Blum, Avrim
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 2 of 50 (2023)

BEYOND PERTURBATIONS: LEARNING GUARANTEES WITH ARBITRARY ADVERSARIAL TEST EXAMPLES

von Goldwasser, Shafi
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 19 of 27 (2021)