| POTs: Protective Optimization Technologies B Kulynych, R Overdorf, C Troncoso, S Gürses Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020 | 107* | 2020 |
| Disparate Vulnerability to Membership Inference Attacks B Kulynych, M Yaghini, G Cherubin, M Veale, C Troncoso Proceedings on Privacy Enhancing Technologies, 460–480, 2022 | 91* | 2022 |
| Participatory approaches to machine learning B Kulynych, D Madras, S Milli, ID Raji, A Zhou, R Zemel International Conference on Machine Learning Workshop 7, 2020 | 44 | 2020 |
| Evading classifiers in discrete domains with provable optimality guarantees B Kulynych, J Hayes, N Samarin, C Troncoso arXiv preprint arXiv:1810.10939, 2018 | 29 | 2018 |
| 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 | 29* | 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 | 23 | 2018 |
| Adversarial Robustness for Tabular Data through Cost and Utility Awareness K Kireev, B Kulynych, C Troncoso NDSS, 2023 | 21 | 2023 |
| Arbitrary Decisions are a Hidden Cost of Differentially Private Training B Kulynych, H Hsu, C Troncoso, FP Calmon FAccT, 2023 | 20 | 2023 |
| What You See is What You Get: Principled Deep Learning via Distributional Generalization B Kulynych, YY Yang, Y Yu, J Błasiok, P Nakkiran NeurIPS, 2022 | 20* | 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 | 7 | 2021 |
| 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 | 7 | 2019 |
| Prediction without Preclusion: Recourse Verification with Reachable Sets A Kothari, B Kulynych, TW Weng, B Ustun ICLR, 2024 | 6 | 2024 |
| Attack-Aware Noise Calibration for Differential Privacy B Kulynych, JF Gomez, G Kaissis, FP Calmon, C Troncoso NeurIPS, 2024 | 5 | 2024 |
| Causal prediction can induce performative stability B Kulynych ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 2022 | 4 | 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 | 4 | 2021 |
| A Scoping Review of Privacy and Utility Metrics in Medical Synthetic Data JLR Bayrem Kaabachi, Jérémie Despraz, Thierry Meurers, Karen Otte, Mehmed ... npj Digital Medicine 8 (50), 2025 | 3 | 2025 |
| Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts G Starke, F Gille, A Termine, YSJ Aquino, R Chavarriaga, A Ferrario, ... Journal of medical Internet research 27, e56306, 2025 | 1 | 2025 |
| Tunable Privacy Risk Evaluation of Generative Adversarial Networks B Kaabachi, F Briki, B Kulynych, J Despraz, JL Raisaro Digital Health and Informatics Innovations for Sustainable Health Care …, 2024 | 1 | 2024 |
| Evaluating Synthetic Data Augmentation to Correct for Data Imbalance in Realistic Clinical Prediction Settings N Wahler, B Kaabachi, B Kulynych, J Despraz, C Simon, JL Raisaro Digital Health and Informatics Innovations for Sustainable Health Care …, 2024 | 1 | 2024 |
| The Fundamental Limits of Least-Privilege Learning T Stadler, B Kulynych, N Papernot, M Gastpar, C Troncoso ICML, 2024 | 1 | 2024 |
Carmela TroncosoEPFLVerified email at epfl.ch
