| Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting S Yeom, I Giacomelli, M Fredrikson, S Jha IEEE Computer Security Foundations Symposium, 268-282, 2018 | 1332 | 2018 |
| FlipTest: Fairness testing via optimal transport E Black, S Yeom, M Fredrikson ACM Conference on Fairness, Accountability, and Transparency, 111-121, 2020 | 122 | 2020 |
| Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning S Yeom, I Giacomelli, A Menaged, M Fredrikson, S Jha Journal of Computer Security 28 (1), 35-70, 2020 | 63 | 2020 |
| Avoiding Disparity Amplification under Different Worldviews S Yeom, MC Tschantz ACM Conference on Fairness, Accountability, and Transparency, 273-283, 2021 | 51* | 2021 |
| Learning fair representations for kernel models Z Tan, S Yeom, M Fredrikson, A Talwalkar International Conference on Artificial Intelligence and Statistics, 155-166, 2020 | 38 | 2020 |
| Individual fairness revisited: Transferring techniques from adversarial robustness S Yeom, M Fredrikson International Joint Conference on Artificial Intelligence, 437-443, 2020 | 30 | 2020 |
| Hunting for discriminatory proxies in linear regression models S Yeom, A Datta, M Fredrikson Neural Information Processing Systems, 4568-4578, 2018 | 22 | 2018 |
| Black-box audits for group distribution shifts M Juárez, S Yeom, M Fredrikson arXiv preprint arXiv:2209.03620, 2022 | 6 | 2022 |
| Black-Box Approaches to Fair Machine Learning S Yeom Carnegie Mellon University, 2021 | | 2021 |
Matt FredriksonCarnegie Mellon UniversityVerified email at cs.cmu.edu
