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Mohammad Yaghini
Mohammad Yaghini
Research Assistant at CleverHans Lab, Vector Institute & University of Toronto
Verified email at mail.utoronto.ca - Homepage
Title
Cited by
Cited by
Year
Non-discriminatory machine learning through convex fairness criteria
N Goel, M Yaghini, B Faltings
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 116-116, 2018
1032018
Dataset inference: Ownership resolution in machine learning
P Maini, M Yaghini, N Papernot
arXiv preprint arXiv:2104.10706, 2021
842021
Proof-of-learning: Definitions and practice
H Jia, M Yaghini, CA Choquette-Choo, N Dullerud, A Thudi, ...
2021 IEEE Symposium on Security and Privacy (SP), 1039-1056, 2021
742021
Disparate vulnerability: On the unfairness of privacy attacks against machine learning
M Yaghini, B Kulynych, G Cherubin, C Troncoso
arXiv e-prints, arXiv: 1906.00389, 2019
442019
SoK: Machine learning governance
V Chandrasekaran, H Jia, A Thudi, A Travers, M Yaghini, N Papernot
arXiv preprint arXiv:2109.10870, 2021
172021
A human-in-the-loop framework to construct context-aware mathematical notions of outcome fairness
M Yaghini, A Krause, H Heidari
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 1023 …, 2021
142021
On the privacy risk of in-context learning
H Duan, A Dziedzic, M Yaghini, N Papernot, F Boenisch
The 61st Annual Meeting Of The Association For Computational Linguistics, 2023
102023
Washing the unwashable: On the (im) possibility of fairwashing detection
A Shahin Shamsabadi, M Yaghini, N Dullerud, S Wyllie, U Aïvodji, ...
Advances in Neural Information Processing Systems 35, 14170-14182, 2022
102022
A human-in-the-loop framework to construct context-dependent mathematical formulations of fairness
M Yaghini, H Heidari, A Krause
arXiv preprint arXiv:1911.03020, 1-25, 2019
82019
Proof-of-learning is currently more broken than you think
C Fang, H Jia, A Thudi, M Yaghini, CA Choquette-Choo, N Dullerud, ...
2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), 797-816, 2023
62023
Learning with impartiality to walk on the pareto frontier of fairness, privacy, and utility
M Yaghini, P Liu, F Boenisch, N Papernot
arXiv preprint arXiv:2302.09183, 2023
32023
Tubes among us: Analog attack on automatic speaker identification
S Ahmed, Y Wani, AS Shamsabadi, M Yaghini, I Shumailov, N Papernot, ...
32nd USENIX Security Symposium (USENIX Security 23), 265-282, 2023
32023
Pipe Overflow: Smashing voice authentication for fun and profit
S Ahmed, Y Wani, AS Shamsabadi, M Yaghini, I Shumailov, N Papernot, ...
arXiv preprint arXiv:2202.02751, 2022
32022
Energy-aware optimization and mechanism design for cellular device-to-device local area networks
MN Soorki, M Yaghini, MH Manshaei, W Saad, H Saidi
2016 Annual Conference on Information Science and Systems (CISS), 309-314, 2016
32016
-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
A Dziedzic, S Rabanser, M Yaghini, A Ale, MA Erdogdu, N Papernot
arXiv preprint arXiv:2207.12545, 2022
22022
Regulation Games for Trustworthy Machine Learning
M Yaghini, P Liu, F Boenisch, N Papernot
arXiv preprint arXiv:2402.03540, 2024
2024
Learning to Walk Impartially on the Pareto Frontier of Fairness, Privacy, and Utility
M Yaghini, P Liu, F Boenisch, N Papernot
NeurIPS 2023 Workshop on Regulatable ML, 2023
2023
FairPATE: Exposing the Pareto Frontier of Fairness, Privacy, Accuracy, and Coverage
M Yaghini, P Liu, F Boenisch, N Papernot
2023
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