Follow
Kevin Stangl
Title
Cited by
Cited by
Year
Recovering from biased data: Can fairness constraints improve accuracy?
A Blum, K Stangl
arXiv preprint arXiv:1912.01094, 2019
972019
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
72022
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
62016
Sequential strategic screening
L Cohen, S Sharifi-Malvajerdi, K Stangl, A Vakilian, J Ziani
International Conference on Machine Learning, 6279-6295, 2023
32023
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
22018
Bayesian Strategic Classification
L Cohen, S Sharifi-Malvajerdi, K Stangl, A Vakilian, J Ziani
arXiv preprint arXiv:2402.08758, 2024
2024
Agnostic Multi-Robust Learning Using ERM
KS Saba Ahmadi, Avrim Blum, Omar Montasser
https://arxiv.org/abs/2303.08944, 2024
2024
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
A Blum, P Okoroafor, A Saha, K Stangl
arXiv preprint arXiv:2307.11892, 2023
2023
The system can't perform the operation now. Try again later.
Articles 1–8