| Certified robustness to adversarial examples with differential privacy M Lecuyer, V Atlidakis, R Geambasu, D Hsu, S Jana 2019 IEEE symposium on security and privacy (SP), 656-672, 2019 | 1086 | 2019 |
| XRay: Enhancing the Web’s Transparency with Differential Correlation M Lécuyer, G Ducoffe, F Lan, A Papancea, T Petsios, R Spahn, ... 23rd USENIX Security Symposium (USENIX Security 14), 49-64, 2014 | 163 | 2014 |
| Sunlight: Fine-grained targeting detection at scale with statistical confidence M Lecuyer, R Spahn, Y Spiliopolous, A Chaintreau, R Geambasu, D Hsu Proceedings of the 22nd ACM SIGSAC conference on computer and communications …, 2015 | 93 | 2015 |
| Synapse: a microservices architecture for heterogeneous-database web applications N Viennot, M Lécuyer, J Bell, R Geambasu, J Nieh Proceedings of the tenth european conference on computer systems, 1-16, 2015 | 73 | 2015 |
| Measuring the effect of training data on deep learning predictions via randomized experiments J Lin, A Zhang, M Lécuyer, J Li, A Panda, S Sen International Conference on Machine Learning, 13468-13504, 2022 | 51 | 2022 |
| Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform M Lécuyer, R Spahn, K Vodrahalli, R Geambasu, D Hsu Proceedings of the 27th ACM Symposium on Operating Systems Principles (SOSP …, 2019 | 49 | 2019 |
| Privacy Budget Scheduling T Luo, M Pan, P Tholoniat, A Cidon, R Geambasu, M Lécuyer 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21), 2021 | 38 | 2021 |
| Harvesting randomness to optimize distributed systems M Lecuyer, J Lockerman, L Nelson, S Sen, A Sharma, A Slivkins Proceedings of the 16th ACM Workshop on Hot Topics in Networks, 178-184, 2017 | 20 | 2017 |
| Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning M Lécuyer arXiv preprint arXiv:2103.01379, 2021 | 18 | 2021 |
| Enhancing selectivity in big data M Lecuyer, R Spahn, R Geambasu, TK Huang, S Sen IEEE Security & Privacy 16 (1), 34-42, 2018 | 17 | 2018 |
| Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization M Lecuyer, R Spahn, R Geambasu, TK Huang, S Sen | 13 | 2017 |
| Improving the transparency of the sharing economy M Lecuyer, M Tucker, A Chaintreau Proceedings of the 26th International Conference on World Wide Web Companion …, 2017 | 12 | 2017 |
| DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation Q Tang, M Lécuyer Trustworthy and Reliable Large-Scale Machine Learning Models Workshop at ICLR, 2023 | 5 | 2023 |
| DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction) Q Tang, F Shpilevskiy, M Lécuyer Proceedings of the AAAI Conference on Artificial Intelligence 38 (14), 15276 …, 2024 | 4 | 2024 |
| GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning S He, Q Yan, F Wu, L Wang, M Lécuyer, I Beschastnikh Proceedings of Machine Learning and Systems 5, 2023 | 4 | 2023 |
| Packing privacy budget efficiently P Tholoniat, K Kostopoulou, M Chowdhury, A Cidon, R Geambasu, ... arXiv preprint arXiv:2212.13228, 2022 | 4 | 2022 |
| Weaving a safe web of news K Kiscuitwala, W Bult, M Lécuyer, TJ Purtell, MKB Ross, A Chaintreau, ... Proceedings of the 22nd International Conference on World Wide Web, 849-852, 2013 | 4 | 2013 |
| PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining M Kazmi, H Lautraite, A Akbari, M Soroco, Q Tang, T Wang, S Gambs, ... arXiv preprint arXiv:2402.09477, 2024 | 3 | 2024 |
| Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design A Behrouz, M Lécuyer, C Rudin, M Seltzer CEUR workshop proceedings 3318, 2022 | 3 | 2022 |
| Web transparency for complex targeting: Algorithms, limits, and tradeoffs G Ducoffe, M Lécuyer, A Chaintreau, R Geambasu Proceedings of the 2015 ACM SIGMETRICS International Conference on …, 2015 | 3 | 2015 |
Roxana GeambasuAssistant Professor of Computer Science, Columbia UniversityVerified email at columbia.edu
