Bogdan Kulynych
Cited by
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POTs: Protective Optimization Technologies
B Kulynych, R Overdorf, C Troncoso, S Gürses
Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020
Disparate vulnerability to membership inference attacks
B Kulynych, M Yaghini, G Cherubin, M Veale, C Troncoso
arXiv preprint arXiv:1906.00389, 2019
ClaimChain: improving the security and privacy of in-band key distribution for messaging
B Kulynych, M Isaakidis, C Troncoso, G Danezis
Proceedings of the 2018 Workshop on Privacy in the Electronic Society, 86-103, 2018
Questioning the assumptions behind fairness solutions
R Overdorf, B Kulynych, E Balsa, C Troncoso, S Gürses
NeurIPS 2018 “Critiquing and Correcting Trends in Machine Learning” Workshop, 2018
Evading classifiers in discrete domains with provable optimality guarantees
B Kulynych, J Hayes, N Samarin, C Troncoso
arXiv preprint arXiv:1810.10939, 2018
mia: A library for running membership inference attacks against ML models
B Kulynych, M Yaghini
Sept, 2018
zksk: A library for composable zero-knowledge proofs
W Lueks, B Kulynych, J Fasquelle, S Le Bail-Collet, C Troncoso
Proceedings of the 18th ACM Workshop on Privacy in the Electronic Society, 50-54, 2019
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning
B Kulynych, YY Yang, Y Yu, J Błasiok, P Nakkiran
arXiv preprint arXiv:2204.03230, 2022
Exploring Data Pipelines through the Process Lens: a Reference Model for Computer Vision
A Balayn, B Kulynych, S Guerses
CVPR 2021 “Beyond Fair Computer Vision” Workshop, 2021
Causal Prediction Can Induce Performative Stability
B Kulynych
ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 2022
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
K Kireev, B Kulynych, C Troncoso
arXiv preprint arXiv:2208.13058, 2022
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
K Albert, M Delano, B Kulynych, RSS Kumar
ICML 2021 “A Blessing in Disguise: The Prospects and Perils of Adversarial …, 2021
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