Recovering from biased data: Can fairness constraints improve accuracy? A Blum, K Stangl arXiv preprint arXiv:1912.01094, 2019 | 98 | 2019 |
Multi stage screening: Enforcing fairness and maximizing efficiency in a pre-existing pipeline A Blum, K Stangl, A Vakilian Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 7 | 2022 |
A practical study of longitudinal reference based compressed sensing for MRI S Birns, B Kim, S Ku, K Stangl, D Needell arXiv preprint arXiv:1608.04728, 2016 | 6 | 2016 |
Sequential strategic screening L Cohen, S Sharifi-Malvajerdi, K Stangl, A Vakilian, J Ziani International Conference on Machine Learning, 6279-6295, 2023 | 3 | 2023 |
Upper and lower bounds on the speed of a one-dimensional excited random walk E Madden, B Kidd, O Levin, J Peterson, J Smith, KM Stangl Involve, a Journal of Mathematics 12 (1), 97-115, 2018 | 2 | 2018 |
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 |
On the Vulnerability of Fairness Constrained Learning to Malicious Noise A Blum, P Okoroafor, A Saha, KM Stangl International Conference on Artificial Intelligence and Statistics, 4096-4104, 2024 | | 2024 |
Bayesian Strategic Classification L Cohen, S Sharifi-Malvajerdi, K Stangl, A Vakilian, J Ziani arXiv preprint arXiv:2402.08758, 2024 | | 2024 |