Mathias Lécuyer
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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
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
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
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
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
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
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
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
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning
M Lécuyer
arXiv preprint arXiv:2103.01379, 2021
Enhancing selectivity in big data
M Lecuyer, R Spahn, R Geambasu, TK Huang, S Sen
IEEE Security & Privacy 16 (1), 34-42, 2018
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
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
M Lecuyer, R Spahn, R Geambasu, TK Huang, S Sen
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
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
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
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
Packing privacy budget efficiently
P Tholoniat, K Kostopoulou, M Chowdhury, A Cidon, R Geambasu, ...
arXiv preprint arXiv:2212.13228, 2022
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
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
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
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