Bogdan Kulynych

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Carmela TroncosoEPFLVerified email at epfl.ch
Seda GursesAssociate Professor TPM, TU Delft, Affiliate COSIC/KU Leuven,Verified email at tudelft.nl
Giovanni CherubinMicrosoft ResearchVerified email at microsoft.com
Michael VealeAssociate Professor in Digital Rights & Regulation, University College LondonVerified email at ucl.ac.uk
Preetum NakkiranApple ML ResearchVerified email at cs.harvard.edu
Yaodong YuUniversity of California, BerkeleyVerified email at berkeley.edu
Yao-Yuan YangDeepMindVerified email at deepmind.com
Jarosław BłasiokETHVerified email at inf.ethz.ch
Klim KireevDoctoral student, EPFLVerified email at epfl.ch
Ram Shankar Siva KumarMicrosoftVerified email at microsoft.com
Kendra AlbertHarvard Law SchoolVerified email at law.harvard.edu
Maggie DelanoAssistant Professor of Engineering, Swarthmore CollegeVerified email at swarthmore.edu
Flavio du Pin CalmonHarvard UniversityVerified email at seas.harvard.edu
Hsiang HsuHarvard UniversityVerified email at g.harvard.edu
Agathe BalaynPhD candidate, Delft University of TechnologyVerified email at tudelft.nl

Bogdan Kulynych

EPFL

Verified email at epfl.ch - Homepage

Trustworthy ML ML Security Privacy Fairness


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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	78*	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	65*	2022
Evading classifiers in discrete domains with provable optimality guarantees B Kulynych, J Hayes, N Samarin, C Troncoso arXiv preprint arXiv:1810.10939, 2018	23	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	23*	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	20	2018
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	9*	2022
Adversarial Robustness for Tabular Data through Cost and Utility Awareness K Kireev, B Kulynych, C Troncoso NDSS, 2023	8	2023
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	5	2021
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B Kulynych, H Hsu, C Troncoso, FP Calmon FAccT, 2023	4	2023
Causal prediction can induce performative stability B Kulynych ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 2022	4	2022
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	4	2019
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	3	2021
Prediction without Preclusion: Recourse Verification with Reachable Sets A Kothari, B Kulynych, TW Weng, B Ustun arXiv preprint arXiv:2308.12820, 2023		2023
Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning B Kulynych EPFL, 2023		2023

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