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Sam Yeom
Sam Yeom
Carnegie Mellon University
Zweryfikowany adres z cs.cmu.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
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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
13322018
FlipTest: Fairness testing via optimal transport
E Black, S Yeom, M Fredrikson
ACM Conference on Fairness, Accountability, and Transparency, 111-121, 2020
1222020
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
632020
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
382020
Individual fairness revisited: Transferring techniques from adversarial robustness
S Yeom, M Fredrikson
International Joint Conference on Artificial Intelligence, 437-443, 2020
302020
Hunting for discriminatory proxies in linear regression models
S Yeom, A Datta, M Fredrikson
Neural Information Processing Systems, 4568-4578, 2018
222018
Black-box audits for group distribution shifts
M Juárez, S Yeom, M Fredrikson
arXiv preprint arXiv:2209.03620, 2022
62022
Black-Box Approaches to Fair Machine Learning
S Yeom
Carnegie Mellon University, 2021
2021
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